-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  D:\Dropbox\Industry_Brazil\Final_Submission\APST_ReplicationFiles\APST_replication tables.log
  log type:  text
 opened on:  15 Dec 2015, 17:50:11

. 
. ******************************************************************************
. 
. *       Replication file for "Agricultural Productivity and Structural Transformation"
. *               by Paula Bustos, Bruno Caprettini and Jacopo Ponticelli
. 
. *       FIRST VERSION   March      31, 2015
. *       THIS VERSION    December   15, 2015
. 
. 
. *       LAST REVISOR            BC
.                 
. *           This file reproduces and exports all regressions included in the paper:
. *               "Agricultural Productivity and Structural Transformation"
. *       Forthcoming on the American Economic Review.
. 
. *       Note: all tables and regressions are produced using the APST_AMC.dta database except:
. *             - Table  1: aggregate statistics from Agricultural Census.
. *             - Table A7: robustness check with regressions at microregion level
. *             - Table A9: robustness check with data from firm-level databases (PIA)
. *       Replication code for table A7 is at the bottom of this file, where the procedure uploads the APST_micro.dta database
. *       Table 1 is not replicated here, as the statistics come directly from Agricultural Censuses publications (see paper for details)
. *       For Table A9 we report the code needed to run the regressions. However, the PIA data can not be distributed and must be accessed on IBGE premises.
. *******************************************************************************
. 
. 
. ****************************************************************************************************
. ****                                   PLAN OF THE PROCEDURE                                    ****
. ****��������������������������������������������������������������������������������������������****
. ****                                                                                            ****
. ****____________________________________________________________________________________________****
. **** Results with data at AMC level:                                                            ****
. ****��������������������������������������������������������������������������������������������****
. ****                                     Baseline Results                                       ****
. ****��������������������������������������������������������������������������������������������****
. **** Table  1: Land use and labor intensity by agricultural activity                            ****
. **** Table  2: Summary Statistics of main variables at municipality level                       ****
. **** Table  3: Basic correlation in the data: agriculture                                       ****
. **** Table  4: Basic correlation in the data: manufacturing                                     ****
. **** Table  5: Comparing Municipalities below/above median increase in potential soy yield      ****
. **** Table  6: The effect of technological change on agriculture. GE soy adoption               ****
. **** Table  7: The effect of technological change on agriculture. Soy and maize expansion       ****
. **** Table  8: The effect of technological change on agriculture                                ****
. ****           Productivity, labor intensity and employment share                               ****
. **** Table  9: The effect of agricultural technological change on manufacturing                 ****
. ****           Employment share, employment and wages                                           ****
. **** Table 10: The effect of agricultural technological change on employment shares             ****
. **** Table 11: Variable factor endowments                                                       ****
. ****                                                                                            ****
. ****                           Robustness results in appendix                                   ****
. ****��������������������������������������������������������������������������������������������****
. **** Table  A1: The effect of agricultural technological change on employment shares            ****
. ****            Observations are weighted by share of aggregate employment                      ****
. **** Table  A2: Basic correlation in the data                                                   ****
. ****            Income per Capita and Services Employment Share                                 ****
. **** Table  A3: The Effect of Agricultural Technical Change on                                  ****
. ****            Income per Capita and Services Employment Share                                 ****
. **** Table  A4: The effect of technological change on agriculture                               ****
. ****            Robustness to controlling for additional initial municipality characteristics   ****
. ****            Panel A.                                                                        ****
. ****            Panel B.                                                                        ****
. **** Table  A5: Effect of technological change on manufacturing                                 ****
. ****            Robustness to controlling for additional initial municipality characteristics   ****
. **** Table  A6: The effect of agricultural technological change on manufacturing and migration  ****
. ****            Robustness to controlling for pre-existing trends                               ****
. **** Table  A8: The effect of agricultural technological change on manufacturing                ****
. ****            Robustness to excluding sectors directly linked to soy and maize                ****
. **** Table A10: The effect of technological change on agriculture. Soy and maize expansion      ****
. ****            Robustness to correcting standard errors for spatial correlation                ****
. ****            a.   Robust standard errors                                                     ****
. ****            b.   Standard errors clustered at micro-region level                            ****
. ****            c.   Standard errors clustered at meso-region level                             ****
. ****            d.   Conley's standard errors: cutoff at 50 Km                                  ****
. ****            e.   Conley's standard errors: cutoff at 100 Km                                 ****
. ****            f.   Conley's standard errors: cutoff at 200 Km                                 ****
. **** Table A11: The effect of technological change on agriculture                               ****
. ****            Productivity, labor intensity and employment share                              ****
. ****            Robustness to correcting standard errors for spatial correlation                ****
. ****            a.   Robust standard errors                                                     ****
. ****            b.   Standard errors clustered at micro-region level                            ****
. ****            c.   Standard errors clustered at meso-region level                             ****
. ****            d.   Conley's standard errors: cutoff at 50 Km                                  ****
. ****            e.   Conley's standard errors: cutoff at 100 Km                                 ****
. ****            f.   Conley's standard errors: cutoff at 200 Km                                 ****
. **** Table A12: The effect of agricultural technological change on manufacturing                ****
. ****            Employment share, employment and wages                                          ****
. ****            Robustness to correcting standard errors for spatial correlation                ****
. ****            a.   Robust standard errors                                                     ****
. ****            b.   Standard errors clustered at micro-region level                            ****
. ****            c.   Standard errors clustered at meso-region level                             ****
. ****            d.   Conley's standard errors: cutoff at 50 Km                                  ****
. ****            e.   Conley's standard errors: cutoff at 100 Km                                 ****
. ****            f.   Conley's standard errors: cutoff at 200 Km                                 ****
. **** Table A13: The effect of technological change on agriculture and manufacturing             ****
. ****            Robustness to Alternative Definition of Technical Change                        ****
. ****                                                                                            ****
. ****____________________________________________________________________________________________****
. **** Results with data at microregion level:                                                    ****
. ****��������������������������������������������������������������������������������������������****
. ****                           Robustness results in appendix                                   ****
. ****��������������������������������������������������������������������������������������������****
. **** Table A7: The effect of agricultural technological change on manufacturing                 ****
. ****           Robustness to using a larger unit of observation: micro-regions                  ****
. ****                                                                                            ****
. ****____________________________________________________________________________________________****
. **** Results with data at from the PIA industrial survey:                                       ****
. ****��������������������������������������������������������������������������������������������****
. ****                           Robustness results in appendix                                   ****
. ****��������������������������������������������������������������������������������������������****
. **** Table A9: The effect of agricultural technological change on manufacturing                 ****
. ****           Robustness of results reported in Table 9 to controlling for commodity prices    ****
. ****************************************************************************************************
. 
. ****************************************************************************************************
. **** Table  1: Land use and labor intensity by agricultural activity                            ****
. ****************************************************************************************************
. * The data for this table has been downloaded directly from the IBGE Sidra repository.
. * To download tables from the 1996 and 2006 Agricultural Census go to: http://www.sidra.ibge.gov.br/
. * See section A2 of the appendix for a detailed description of the data shown in this table.
. 
. ****************************************************************************************************
. **** Table  2: Summary Statistics of main variables at municipality level                       ****
. ****************************************************************************************************
. use APST_AMC, replace

. 
. de

Contains data from APST_AMC.dta
  obs:        29,820                          
 vars:            71                          15 Dec 2015 15:43
 size:     7,633,920                          
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
              storage   display    value
variable name   type    format     label      variable label
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
year            int     %9.0g                 Year
time            byte    %9.0g                 Period indicator (1980=0; 1985=1; 1991=2; 1996=3; 2000=4; 2006=5; 2010=6)
y2010           byte    %9.0g                 =1 in 2010
cod_uf          byte    %19.0g     uf_lbl     IBGE official Federal Unit code
AMC             int     %9.0g                 Area Minima Comparavel (minimum comparable area)
cod_amc         str12   %12s                  AMC code (from IBGE, but updated for the period 1997-2010)
micro           long    %10.0g                Microregion
meso            int     %10.0g                Mesoregion
fs              byte    %9.0g                 Sample selector: sample for tables 7, 8 & C7 (N=3652)
ols             byte    %9.0g                 Sample selector: sample for tables 4 & 5 (N=3765)
rf              byte    %9.0g                 Sample selector: sample for tables 9, 10, 11, 12, C7, C8 & C9 (N=4149)
pre             byte    %9.0g                 Sample selector: sample for table C3 (N=7984)
frontier        byte    %25.0g     f          =1 if AMC is agricultural frontier
constant        byte    %9.0g                 =1 always (used in the Conley regressions)
latitude        float   %9.0g                 Latitude of the centroid of the Area Minima Comparavel
longitude       float   %9.0g                 Longitude of the centroid of the Area Minima Comparavel
soy_TA          float   %9.0g                 Share of land in farm harvested with soy
mze_TA          float   %9.0g                 Share of land in farm harvested with maize
gsoy_TA         float   %9.0g                 Share of land in farm harvested with GE soy
nsoy_TA         float   %9.0g                 Share of land in farm harvested with non-GE soy
log_PQ_LA       float   %9.0g                 Log value of output per worker in agriculture
log_LA_TA       float   %9.0g                 Log workers per hectare of land in agriculture
dsoy_TA         float   %9.0g                 Change in % of land in farm harvested with soy
dmze_TA         float   %9.0g                 Change in % of land in farm harvested with maize
dgsoy_TA        float   %9.0g                 Change in % of land in farm harvested with GE soy
dnsoy_TA        float   %9.0g                 Change in % of land in farm harvested with non-GE soy
dsoy_TA_w       float   %9.0g                 Change in % of land in farm harvested with soy (winsorized 1%)
dmze_TA_w       float   %9.0g                 Change in % of land in farm harvested with maize (winsorized 1%)
dgsoy_TA_w      float   %9.0g                 Change in % of land in farm harvested with GE soy (winsorized 1%)
dnsoy_TA_w      float   %9.0g                 Change in % of land in farm harvested with non-GE soy (winsorized 1%)
dlog_PQ_LA      float   %9.0g                 % change value of output per worker in agriculture
dlog_LA_TA      float   %9.0g                 % change workers per hectare of land in agriculture
soy_FamSh       float   %9.0g                 Share of soy farms under 'Agricultura Familiar' in 2006
dsoy_TA_w_FamSh float   %9.0g                 % change of soy farmland � % of soy farms under 'Agricultura Familiar'
dA_soy_FamSh    float   %9.0g                 Technical change in soy � % of soy farms under 'Agricultura Familiar'
La_L            float   %9.0g                 Share of 16-55 workers in agriculture
Lm_L            float   %9.0g                 Share of 16-55 workers in manufacturing
Ls_L            float   %9.0g                 Share of 16-55 workers in services
Lr_L            float   %9.0g                 Share of 16-55 workers in other sectors
log_Lm          float   %9.0g                 Log of 16-55 workers in manufacturing
log_ym          float   %9.0g                 Log of average wage in manufacturing (in R$2000)
dLa_L           float   %9.0g                 Change in % of 16-55 workers in agriculture
dLm_L           float   %9.0g                 Change in % of 16-55 workers in manufacturing
dLs_L           float   %9.0g                 Change in % of 16-55 workers in services
dLr_L           float   %9.0g                 Change in % of 16-55 workers in other sectors
dLm0_L          float   %9.0g                 Change in % of 16-55 workers in manuf. sectors not linked to soy & maize
dlog_Lm         float   %9.0g                 % change of 16-55 workers in manufacturing (91-00: old classification)
dlog_Lm0        float   %9.0g                 % change of 16-55 workers in manuf. sectors not linked to soy & maize
dlog_ym         float   %9.0g                 % change of average wage in manufacturing (in R$2000; 91-00: old classification)
dlog_ym0        float   %9.0g                 % change of average wage in manuf. sectors not linked to soy & maize (in R$2000)
migration_rate  float   %9.0g                 Net migration rate of people aged 16-55
A_soy_l         float   %9.0g                 Potential yield in soy: low inputs
A_soy_h         float   %9.0g                 Potential yield in soy: high inputs
A_mze_l         float   %9.0g                 Potential yield in maize: low inputs
A_mze_h         float   %9.0g                 Potential yield in maize: high inputs
dA_soy          float   %9.0g                 Technical change in soy
dA_mze          float   %9.0g                 Technical change in maize
dA_soy_p50      byte    %8.0g                 =1 if above median technical change in soy
dA_soy_y2010    float   %9.0g                 Technical change in soy in 2010, 0 otherwise
dA_mze_y2010    float   %9.0g                 Technical change in maize in 2010, 0 otherwise
dA_soy2         float   %9.0g                 Technical change in soy: high - medium inputs
dA_mze2         float   %9.0g                 Technical change in maize: high - medium inputs
rural_adult     float   %9.0g                 % of population living in rural areas
log_y_pc_r      float   %9.0g                 Log Income per capita (in R$2000)
alpha_adult     float   %9.0g                 % of population who can read and write
log_pop_area    float   %9.0g                 Log Population density
log_ya_sct91    float   %9.0g                 Log of average wage in agriculture (in R$2000; old classification)
La_L_sct91      float   %9.0g                 Share of 16-55 workers in agriculture (old sector classification)
Lm_L_sct91      float   %9.0g                 Share of 16-55 workers in agriculture (old sector classification)
weight          float   %9.0g                 Share of aggregate Brazilian employment in 2000
dlog_y          float   %9.0g                 % change of average income from any source (in R$2000)
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Sorted by:  AMC  time

. tabstat  soy_TA  mze_TA  gsoy_TA  nsoy_TA  log_PQ_LA      log_LA_TA , by(year) stat(N) 

Summary statistics: N
  by categories of: year (Year)

    year |    soy_TA    mze_TA   gsoy_TA   nsoy_TA  log_PQ~A  log_LA~A
---------+------------------------------------------------------------
    1980 |         0         0         0         0         0         0
    1985 |         0         0         0         0      4217         0
    1991 |         0         0         0         0         0         0
    1996 |      4243      4243      4243      4243      4242      4243
    2000 |         0         0         0         0         0         0
    2006 |      3928      4118      4063      3777         0      4239
    2010 |         0         0         0         0         0         0
---------+------------------------------------------------------------
   Total |      8171      8361      8306      8020      8459      8482
----------------------------------------------------------------------

. tabstat dsoy_TA dmze_TA dgsoy_TA dnsoy_TA dlog_PQ_LA     dlog_LA_TA , by(year) stat(N)

Summary statistics: N
  by categories of: year (Year)

    year |   dsoy_TA   dmze_TA  dgsoy_TA  dnsoy_TA  dlog_P~A  dlog_L~A
---------+------------------------------------------------------------
    1980 |         0         0         0         0         0         0
    1985 |         0         0         0         0         0         0
    1991 |         0         0         0         0         0         0
    1996 |         0         0         0         0         0         0
    2000 |         0         0         0         0         0         0
    2006 |      3922      4113      4057      3771      4232      4233
    2010 |         0         0         0         0         0         0
---------+------------------------------------------------------------
   Total |      3922      4113      4057      3771      4232      4233
----------------------------------------------------------------------

. tabstat  La_L  Lm_L  Ls_L  Lr_L         log_Lm           log_ym     , by(year) stat(N)

Summary statistics: N
  by categories of: year (Year)

    year |      La_L      Lm_L      Ls_L      Lr_L    log_Lm    log_ym
---------+------------------------------------------------------------
    1980 |         0         0         0         0         0         0
    1985 |         0         0         0         0         0         0
    1991 |         0         0         0         0         0         0
    1996 |         0         0         0         0         0         0
    2000 |      4260      4260      4260      4260      4255      4228
    2006 |         0         0         0         0         0         0
    2010 |      4260      4260      4260      4260      4256      4249
---------+------------------------------------------------------------
   Total |      8520      8520      8520      8520      8511      8477
----------------------------------------------------------------------

. tabstat dLa_L dLm_L dLs_L dLr_L dLm0_L dlog_Lm dlog_Lm0 dlog_ym dlog_ym0, by(year)

Summary statistics: mean
  by categories of: year (Year)

    year |     dLa_L     dLm_L     dLs_L     dLr_L    dLm0_L   dlog_Lm  dlog_Lm0   dlog_ym  dlog_ym0
---------+------------------------------------------------------------------------------------------
    1980 |         .         .         .         .         .         .         .         .         .
    1985 |         .         .         .         .         .         .         .         .         .
    1991 |         .         .         .         .         .         .         .         .         .
    1996 |         .         .         .         .         .         .         .         .         .
    2000 |         .         .         .         .         .  .2437421         .  .2059334         .
    2006 |         .         .         .         .         .         .         .         .         .
    2010 | -.0634836  .0132876  .0324924  .0177036  .0086839  .2206182  .2241082  .2847206  .3069632
---------+------------------------------------------------------------------------------------------
   Total | -.0634836  .0132876  .0324924  .0177036  .0086839  .2321488  .2241082  .2460446  .3069632
----------------------------------------------------------------------------------------------------

. tabstat  migration_rate A_soy_l A_soy_h A_mze_l A_mze_h dA_soy dA_mze dA_soy_p50 dA_soy_y2010 dA_mze_y2010 dA_soy2 dA_mze2 rural_adult log_y_pc_r alpha_adult log_pop_a
> rea log_ya_sct91 La_L_sct91 Lm_L_sct91, by(year)

Summary statistics: mean
  by categories of: year (Year)

    year |  migrat~e   A_soy_l   A_soy_h   A_mze_l   A_mze_h    dA_soy    dA_mze  dA_so~50  dA_so~10  dA_mze~0   dA_soy2   dA_mze2  rural_~t  log_y_~r  alpha_~t
---------+------------------------------------------------------------------------------------------------------------------------------------------------------
    1980 |         .  .3008231  2.104793   .988622  4.046567   1.80397  3.057945         .         0         0  1.240125  2.156944   .549499   4.15648  .6499686
    1985 |         .         .         .         .         .         .         .         .         .         .         .         .         .         .         .
    1991 |         .  .3008231  2.104793   .988622  4.046567   1.80397  3.057945  1.499879         0         0  1.240125  2.156944  .4577306  4.524725  .7173189
    1996 |         .  .3008231  2.104793   .988622  4.046567   1.80397  3.057945         .         0         0  1.240125  2.156944         .         .         .
    2000 | -.0370542  .3008231  2.104793   .988622  4.046567   1.80397  3.057945         .         0         0  1.240125  2.156944         .         .         .
    2006 |         .  .3008231  2.104793   .988622  4.046567   1.80397  3.057945         .         0         0  1.240125  2.156944         .         .         .
    2010 | -.0234084  .3008231  2.104793   .988622  4.046567   1.80397  3.057945  1.499879   1.80397  3.057945  1.240125  2.156944         .         .         .
---------+------------------------------------------------------------------------------------------------------------------------------------------------------
   Total | -.0302313  .3008231  2.104793   .988622  4.046567   1.80397  3.057945  1.499879  .3006617  .5096575  1.240125  2.156944  .5035717  4.340797  .6836794
----------------------------------------------------------------------------------------------------------------------------------------------------------------

    year |  log_po~a  log_y~91  La_L_~91  Lm_L_~91
---------+----------------------------------------
    1980 |   3.09484         .         .         .
    1985 |         .         .         .         .
    1991 |  3.201843  5.102136  .4692584  .0892448
    1996 |         .         .         .         .
    2000 |         .  5.263878         .         .
    2006 |         .         .         .         .
    2010 |         .         .         .         .
---------+----------------------------------------
   Total |  3.148398  5.184696  .4692584  .0892448
--------------------------------------------------

. 
. * PANEL A: value per worker, labor intensity, soy area share, maize area share, GE soy area share
. tabstat  log_PQ_LA          log_LA_TA               if year == 1996 & rf  == 1, stat(mean sd  )

   stats |  log_PQ~A  log_LA~A
---------+--------------------
    mean |  7.689557 -2.584994
      sd |  1.191577  1.047649
------------------------------

. tabstat dlog_PQ_LA     dlog_LA_TA                   if year == 2006 & rf  == 1, stat(mean sd N)

   stats |  dlog_P~A  dlog_L~A
---------+--------------------
    mean |  .5610235 -.0265155
      sd |  .8105362  .5507905
       N |      4149      4149
------------------------------

. tabstat  soy_TA         mze_TA     gsoy_TA      if year == 1996 & fs  == 1, stat(mean sd N)

   stats |    soy_TA    mze_TA   gsoy_TA
---------+------------------------------
    mean |  .0265707  .0491403         0
      sd |  .0968414  .0679586         0
       N |      3652      3652      3652
----------------------------------------

. tabstat dsoy_TA        dmze_TA    dgsoy_TA      if year == 2006 & fs  == 1, stat(mean sd N)

   stats |   dsoy_TA   dmze_TA  dgsoy_TA
---------+------------------------------
    mean |  .0128183  .0097941  .0149269
      sd |  .0622416  .0933603    .07494
       N |      3652      3652      3652
----------------------------------------

. 
. * PANEL B: EMPLOYMENT SHARES
. tabstat  La_L  Lm_L  Ls_L  Lr_L  log_Lm  log_ym if year == 2000 & rf  == 1, stat(mean sd  )     

   stats |      La_L      Lm_L      Ls_L      Lr_L    log_Lm    log_ym
---------+------------------------------------------------------------
    mean |  .3827818  .1036745  .3622958  .1512479  5.885274  5.541225
      sd |   .188612   .090192  .1359401  .0537556  1.580258  .5003442
----------------------------------------------------------------------

. tabstat dLa_L dLm_L dLs_L dLr_L dlog_Lm dlog_ym if year == 2010 & rf  == 1, stat(mean sd N)

   stats |     dLa_L     dLm_L     dLs_L     dLr_L   dlog_Lm   dlog_ym
---------+------------------------------------------------------------
    mean | -.0635909  .0137656  .0322513   .017574  .2211664  .2868942
      sd |  .0737864  .0568754  .0565372  .0381849  .6076156  .3648363
       N |      4149      4149      4149      4149      4149      4149
----------------------------------------------------------------------

. 
. * PANEL C: MIGRATION RATE
. tabstat  migration_rate                         if year == 2000 & pre == 1, stat(mean sd N) 

    variable |      mean        sd         N
-------------+------------------------------
migration_~e | -.0357699  .1814604      3992
--------------------------------------------

. tabstat  migration_rate                         if year == 2010 & rf  == 1, stat(mean sd N) 

    variable |      mean        sd         N
-------------+------------------------------
migration_~e |  -.023702   .124096      4149
--------------------------------------------

. 
. * PANEL D: TECHNOLOGICAL CHANGE
. tabstat A_soy_l A_soy_h A_mze_l A_mze_h dA_soy dA_mze if year == 2010 & rf== 1, stat(mean sd N) col(stat)

    variable |      mean        sd         N
-------------+------------------------------
     A_soy_l |  .3017499   .154485      4149
     A_soy_h |  2.112541  .9380331      4149
     A_mze_l |  .9924047  .4940302      4149
     A_mze_h |  4.065888  2.197034      4149
      dA_soy |  1.810791  .8510358      4149
      dA_mze |  3.073483  1.811407      4149
--------------------------------------------

. 
. ****************************************************************************************************
. **** Table  3: Basic correlation in the data: agriculture                                       ****
. ****************************************************************************************************
. reg dlog_PQ_LA     dsoy_TA_w     dmze_TA_w                if year == 2006 & ols == 1, r 

Linear regression                                      Number of obs =    3765
                                                       F(  2,  3762) =   43.59
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0221
                                                       Root MSE      =  .78834

------------------------------------------------------------------------------
             |               Robust
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   dsoy_TA_w |   .5898066   .2474297     2.38   0.017     .1046973    1.074916
   dmze_TA_w |   1.650928    .197794     8.35   0.000     1.263134    2.038722
       _cons |   .5477225   .0135815    40.33   0.000     .5210948    .5743503
------------------------------------------------------------------------------

.         outreg2 using Tables\Table3, ctitle("Change in log value per worker (2006-1996)") dec(3) nocons replace
dir : seeout

. reg dlog_LA_TA     dsoy_TA_w     dmze_TA_w              if year == 2006 & ols == 1, r

Linear regression                                      Number of obs =    3765
                                                       F(  2,  3762) =   21.03
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0084
                                                       Root MSE      =   .5246

------------------------------------------------------------------------------
             |               Robust
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   dsoy_TA_w |  -.4793612   .1538652    -3.12   0.002    -.7810286   -.1776938
   dmze_TA_w |   .7370328   .1193078     6.18   0.000     .5031185    .9709471
       _cons |  -.0292788    .009163    -3.20   0.001    -.0472437   -.0113139
------------------------------------------------------------------------------

.         outreg2 using Tables\Table3, ctitle("Change in log labor intensity (2006-1996)")  dec(3) nocons append  
dir : seeout

. reg dLa_L        l.dsoy_TA_w   l.dmze_TA_w      if year == 2010 &       ols == 1, r

Linear regression                                      Number of obs =    3765
                                                       F(  2,  3762) =    7.13
                                                       Prob > F      =  0.0008
                                                       R-squared     =  0.0033
                                                       Root MSE      =  .07362

------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   dsoy_TA_w |
         L1. |  -.0900117   .0267489    -3.37   0.001    -.1424555    -.037568
             |
   dmze_TA_w |
         L1. |  -.0140342   .0190532    -0.74   0.461    -.0513899    .0233215
             |
       _cons |  -.0624578   .0012558   -49.73   0.000      -.06492   -.0599956
------------------------------------------------------------------------------

.         outreg2 using Tables\Table3, ctitle("Change in employment share (2010-2000)")     dec(3) nocons append excel
Tables\Table3.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table  4: Basic correlation in the data: manufacturing                                     ****
. ****************************************************************************************************
. reg dLm_L   l.dsoy_TA_w   l.dmze_TA_w   if year == 2010 & ols== 1, r

Linear regression                                      Number of obs =    3765
                                                       F(  2,  3762) =   11.21
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0070
                                                       Root MSE      =  .05501

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   dsoy_TA_w |
         L1. |   .1056897   .0224557     4.71   0.000     .0616631    .1497163
             |
   dmze_TA_w |
         L1. |   .0009061   .0127846     0.07   0.943    -.0241593    .0259716
             |
       _cons |   .0110237   .0009426    11.70   0.000     .0091757    .0128718
------------------------------------------------------------------------------

.         outreg2 using Tables\Table4, ctitle("Change in Employment share (2010-2000)") dec(3) nocons replace
dir : seeout

. reg dlog_Lm l.dsoy_TA_w   l.dmze_TA_w   if year == 2010 & ols== 1, r

Linear regression                                      Number of obs =    3765
                                                       F(  2,  3762) =   11.36
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0059
                                                       Root MSE      =  .60172

------------------------------------------------------------------------------
             |               Robust
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   dsoy_TA_w |
         L1. |    1.05279   .2264028     4.65   0.000     .6089057    1.496674
             |
   dmze_TA_w |
         L1. |   .0183628   .1471871     0.12   0.901    -.2702115    .3069371
             |
       _cons |   .1984053   .0103796    19.11   0.000     .1780551    .2187555
------------------------------------------------------------------------------

.         outreg2 using Tables\Table4, ctitle("Change in Log employment (2010-2000)")   dec(3) nocons append
dir : seeout

. reg dlog_ym l.dsoy_TA_w   l.dmze_TA_w   if year == 2010 & ols== 1, r

Linear regression                                      Number of obs =    3765
                                                       F(  2,  3762) =    0.88
                                                       Prob > F      =  0.4156
                                                       R-squared     =  0.0003
                                                       Root MSE      =  .36592

------------------------------------------------------------------------------
             |               Robust
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   dsoy_TA_w |
         L1. |   .1495179   .1128458     1.32   0.185    -.0717269    .3707627
             |
   dmze_TA_w |
         L1. |  -.0393744   .0797648    -0.49   0.622    -.1957609     .117012
             |
       _cons |   .2881464   .0062154    46.36   0.000     .2759606    .3003322
------------------------------------------------------------------------------

.         outreg2 using Tables\Table4, ctitle("Change in Log wage (2010-2000)")         dec(3) nocons append excel
Tables\Table4.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table  5: Comparing Municipalities below/above median increase in potential soy yield      ****
. ****************************************************************************************************
. ttest La_L_sct91  , by(dA_soy_p50)           /* Note: in 1991 sector of employment was recorded with a different classification - here it is flagged as "_sct91" */

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       1 |    2075    .5001326    .0047731    .2174231    .4907721    .5094931
       2 |    2074    .4431903    .0044992    .2048997    .4343669    .4520138
---------+--------------------------------------------------------------------
combined |    4149    .4716683     .003309    .2131409     .465181    .4781557
---------+--------------------------------------------------------------------
    diff |            .0569423    .0065594                .0440823    .0698023
------------------------------------------------------------------------------
    diff = mean(1) - mean(2)                                      t =   8.6810
Ho: diff = 0                                     degrees of freedom =     4147

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest Lm_L_sct91  , by(dA_soy_p50)           /* Note: in 1991 sector of employment was recorded with a different classification - here it is flagged as "_sct91" */

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       1 |    2075    .0802997     .002089    .0951593    .0762029    .0843965
       2 |    2074    .0971874    .0021028    .0957654    .0930636    .1013113
---------+--------------------------------------------------------------------
combined |    4149    .0887415    .0014877    .0958241    .0858249    .0916581
---------+--------------------------------------------------------------------
    diff |           -.0168878    .0029641                -.022699   -.0110765
------------------------------------------------------------------------------
    diff = mean(1) - mean(2)                                      t =  -5.6974
Ho: diff = 0                                     degrees of freedom =     4147

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest rural_adult , by(dA_soy_p50)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       1 |    2075    .5156968    .0048149    .2193305    .5062542    .5251394
       2 |    2074    .4039108    .0049101     .223611    .3942816      .41354
---------+--------------------------------------------------------------------
combined |    4149    .4598173    .0035459    .2284001    .4528655    .4667691
---------+--------------------------------------------------------------------
    diff |             .111786    .0068769                .0983036    .1252685
------------------------------------------------------------------------------
    diff = mean(1) - mean(2)                                      t =  16.2552
Ho: diff = 0                                     degrees of freedom =     4147

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 1.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 0.0000

. ttest log_y_pc_r  , by(dA_soy_p50)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       1 |    2075    4.389028    .0124093    .5652691    4.364692    4.413363
       2 |    2074    4.655812    .0125423    .5711904    4.631215    4.680409
---------+--------------------------------------------------------------------
combined |    4149    4.522388    .0090606    .5836206    4.504624    4.540151
---------+--------------------------------------------------------------------
    diff |           -.2667847    .0176436               -.3013756   -.2321937
------------------------------------------------------------------------------
    diff = mean(1) - mean(2)                                      t = -15.1207
Ho: diff = 0                                     degrees of freedom =     4147

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest log_pop_area, by(dA_soy_p50)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       1 |    2075    3.155104    .0287243    1.308453    3.098773    3.211435
       2 |    2074    3.218666    .0286293    1.303812     3.16252    3.274811
---------+--------------------------------------------------------------------
combined |    4149    3.186877    .0202811    1.306364    3.147115    3.226639
---------+--------------------------------------------------------------------
    diff |           -.0635614    .0405552               -.1430713    .0159485
------------------------------------------------------------------------------
    diff = mean(1) - mean(2)                                      t =  -1.5673
Ho: diff = 0                                     degrees of freedom =     4147

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0586         Pr(|T| > |t|) = 0.1171          Pr(T > t) = 0.9414

. ttest alpha_adult , by(dA_soy_p50)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       1 |    2075    .6879057    .0037626    .1713956    .6805268    .6952846
       2 |    2074    .7450578    .0033369    .1519661    .7385138    .7516019
---------+--------------------------------------------------------------------
combined |    4149    .7164749    .0025532    .1644575    .7114693    .7214805
---------+--------------------------------------------------------------------
    diff |           -.0571521    .0050293               -.0670122    -.047292
------------------------------------------------------------------------------
    diff = mean(1) - mean(2)                                      t = -11.3639
Ho: diff = 0                                     degrees of freedom =     4147

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. 
. ****************************************************************************************************
. **** Table  6: The effect of technological change on agriculture. GE soy adoption               ****
. ****************************************************************************************************
. reg dgsoy_TA_w                          dA_soy                                  l3.rural_adult                                           if year ==2006 & fs== 1, r 

Linear regression                                      Number of obs =    3652
                                                       F(  2,  3649) =   57.41
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0833
                                                       Root MSE      =  .05714

------------------------------------------------------------------------------
             |               Robust
  dgsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0205803    .001934    10.64   0.000     .0167885    .0243721
             |
 rural_adult |
         L3. |   .0391786   .0049164     7.97   0.000     .0295393    .0488178
             |
       _cons |  -.0414794    .004498    -9.22   0.000    -.0502983   -.0326605
------------------------------------------------------------------------------

.         outreg2 using Tables\Table6, ctitle("Change in GE Soy area share (2006-1996)") dec(3) nocons replace
dir : seeout

. reg dgsoy_TA_w                          dA_soy                                  l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult   if year ==2006 & fs== 1, 
> r 

Linear regression                                      Number of obs =    3652
                                                       F(  5,  3646) =   38.19
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1618
                                                       Root MSE      =  .05466

------------------------------------------------------------------------------
             |               Robust
  dgsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .019046   .0017589    10.83   0.000     .0155975    .0224945
             |
 rural_adult |
         L3. |   .0847196   .0078251    10.83   0.000     .0693777    .1000616
             |
  log_y_pc_r |
         L3. |  -.0004505   .0027818    -0.16   0.871    -.0059045    .0050035
             |
log_pop_area |
         L3. |   .0032022   .0005168     6.20   0.000      .002189    .0042154
             |
 alpha_adult |
         L3. |   .1140301   .0108073    10.55   0.000     .0928411    .1352191
             |
       _cons |  -.1492172   .0143611   -10.39   0.000    -.1773739   -.1210606
------------------------------------------------------------------------------

.         outreg2 using Tables\Table6, ctitle(" ") dec(3) nocons append
dir : seeout

. reg dnsoy_TA_w                          dA_soy                                  l3.rural_adult                                       if year ==2006 & fs== 1, r       

Linear regression                                      Number of obs =    3652
                                                       F(  2,  3649) =   13.26
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0187
                                                       Root MSE      =  .05328

------------------------------------------------------------------------------
             |               Robust
  dnsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0087434   .0017765    -4.92   0.000    -.0122265   -.0052604
             |
 rural_adult |
         L3. |  -.0173329   .0043937    -3.94   0.000    -.0259473   -.0087185
             |
       _cons |    .021218   .0039151     5.42   0.000      .013542     .028894
------------------------------------------------------------------------------

.         outreg2 using Tables\Table6, ctitle("Change in Non-GE Soy area share (2006-1996)") dec(3) nocons append
dir : seeout

. reg dnsoy_TA_w                          dA_soy                                  l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult   if year ==2006 & fs== 1, 
> r       

Linear regression                                      Number of obs =    3652
                                                       F(  5,  3646) =   15.92
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0438
                                                       Root MSE      =  .05261

------------------------------------------------------------------------------
             |               Robust
  dnsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0086845   .0016711    -5.20   0.000    -.0119609   -.0054081
             |
 rural_adult |
         L3. |   -.043548   .0068762    -6.33   0.000    -.0570295   -.0300665
             |
  log_y_pc_r |
         L3. |   .0006971   .0026013     0.27   0.789    -.0044031    .0057973
             |
log_pop_area |
         L3. |  -.0046151   .0005804    -7.95   0.000     -.005753   -.0034772
             |
 alpha_adult |
         L3. |  -.0482138   .0103488    -4.66   0.000    -.0685038   -.0279237
             |
       _cons |   .0792684   .0121883     6.50   0.000     .0553719    .1031648
------------------------------------------------------------------------------

.         outreg2 using Tables\Table6, ctitle(" ") dec(3) nocons append excel
Tables\Table6.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table  7: The effect of technological change on agriculture. Soy and maize expansion       ****
. ****************************************************************************************************
. reg dsoy_TA_w                           dA_soy                          l3.rural_adult                                      if year ==2006 & fs== 1, r

Linear regression                                      Number of obs =    3652
                                                       F(  2,  3649) =   79.54
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0666
                                                       Root MSE      =  .04088

------------------------------------------------------------------------------
             |               Robust
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0133053   .0010822    12.29   0.000     .0111835     .015427
             |
 rural_adult |
         L3. |   .0198788   .0029061     6.84   0.000      .014181    .0255766
             |
       _cons |  -.0210946   .0022652    -9.31   0.000    -.0255358   -.0166533
------------------------------------------------------------------------------

.         outreg2 using Tables\Table7, ctitle("Change in Soy area share (2006-1996)")                                         dec(3) nocons replace
dir : seeout

. reg dsoy_TA_w                           dA_soy                  dA_mze  l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult  if year ==2006 & fs== 1, r

Linear regression                                      Number of obs =    3652
                                                       F(  6,  3645) =   47.49
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1237
                                                       Root MSE      =  .03963

------------------------------------------------------------------------------
             |               Robust
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .012712   .0018971     6.70   0.000     .0089925    .0164315
      dA_mze |  -.0005212   .0007385    -0.71   0.480    -.0019691    .0009267
             |
 rural_adult |
         L3. |   .0394215   .0046957     8.40   0.000      .030215     .048628
             |
  log_y_pc_r |
         L3. |   .0008106   .0022051     0.37   0.713    -.0035127    .0051339
             |
log_pop_area |
         L3. |  -.0017568   .0004981    -3.53   0.000    -.0027334   -.0007802
             |
 alpha_adult |
         L3. |   .0643435   .0066634     9.66   0.000     .0512791    .0774079
             |
       _cons |   -.071237   .0097169    -7.33   0.000    -.0902881   -.0521859
------------------------------------------------------------------------------

.         outreg2 using Tables\Table7, ctitle(" ")                                            dec(3) nocons append
dir : seeout

. reg dmze_TA_w                                           dA_mze  l3.rural_adult                                  if year ==2006 & fs== 1, r

Linear regression                                      Number of obs =    3652
                                                       F(  2,  3649) =   17.17
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0089
                                                       Root MSE      =  .06548

------------------------------------------------------------------------------
             |               Robust
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_mze |   .0032079   .0005907     5.43   0.000     .0020498     .004366
             |
 rural_adult |
         L3. |   .0112139   .0044196     2.54   0.011     .0025488    .0198791
             |
       _cons |  -.0068172   .0027641    -2.47   0.014    -.0122365   -.0013979
------------------------------------------------------------------------------

.         outreg2 using Tables\Table7, ctitle("Change in Maize area share (2006-1996)")                                       dec(3) nocons append
dir : seeout

. reg dmze_TA_w                           dA_soy                  dA_mze  l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult     if year ==2006 & fs== 1, r

Linear regression                                      Number of obs =    3652
                                                       F(  6,  3645) =    9.48
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0150
                                                       Root MSE      =  .06532

------------------------------------------------------------------------------
             |               Robust
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0008667   .0026734     0.32   0.746    -.0043749    .0061083
      dA_mze |   .0028532   .0010878     2.62   0.009     .0007205    .0049859
             |
 rural_adult |
         L3. |   .0100014   .0066597     1.50   0.133    -.0030557    .0230585
             |
  log_y_pc_r |
         L3. |  -.0046405   .0037919    -1.22   0.221    -.0120749    .0027938
             |
log_pop_area |
         L3. |   .0035167   .0006459     5.44   0.000     .0022504     .004783
             |
 alpha_adult |
         L3. |  -.0059855   .0119107    -0.50   0.615    -.0293378    .0173669
             |
       _cons |   .0070924   .0149021     0.48   0.634    -.0221249    .0363096
------------------------------------------------------------------------------

.         outreg2 using Tables\Table7, ctitle(" ") dec(3) nocons append excel sortvar(dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult)
Tables\Table7.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table  8: The effect of technological change on agriculture                                ****
. ****           Productivity, labor intensity and employment share                               ****
. ****************************************************************************************************
. reg dlog_PQ_LA          dA_soy                  dA_mze  l3.rural_adult                                          if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  3,  4145) =   11.52
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0085
                                                       Root MSE      =  .80739

------------------------------------------------------------------------------
             |               Robust
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1155176   .0255745     4.52   0.000     .0653778    .1656574
      dA_mze |  -.0243465   .0117091    -2.08   0.038    -.0473026   -.0013903
             |
 rural_adult |
         L3. |   .2806721   .0614485     4.57   0.000        .1602    .4011442
             |
       _cons |   .2976158   .0492398     6.04   0.000     .2010793    .3941523
------------------------------------------------------------------------------

.         outreg2 using Tables\Table8, ctitle("Change in Log value per worker (2006-1996)") dec(3) nocons replace
dir : seeout

. reg dlog_PQ_LA          dA_soy                  dA_mze  l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult     if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =    7.85
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0121
                                                       Root MSE      =   .8062

------------------------------------------------------------------------------
             |               Robust
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1344694   .0272028     4.94   0.000     .0811373    .1878015
      dA_mze |  -.0331244   .0120088    -2.76   0.006     -.056668   -.0095808
             |
 rural_adult |
         L3. |   .1338137   .0751829     1.78   0.075    -.0135853    .2812126
             |
  log_y_pc_r |
         L3. |  -.0145714   .0477773    -0.30   0.760    -.1082406    .0790977
             |
log_pop_area |
         L3. |  -.0161204   .0123575    -1.30   0.192    -.0403477    .0081068
             |
 alpha_adult |
         L3. |  -.2898411     .14658    -1.98   0.048    -.5772166   -.0024657
             |
       _cons |   .6827405   .1776991     3.84   0.000     .3343548    1.031126
------------------------------------------------------------------------------

.         outreg2 using Tables\Table8, ctitle(" ") dec(3) nocons append
dir : seeout

. reg dlog_LA_TA                  dA_soy                  dA_mze  l3.rural_adult                                          if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  3,  4145) =    6.62
                                                       Prob > F      =  0.0002
                                                       R-squared     =  0.0052
                                                       Root MSE      =  .54957

------------------------------------------------------------------------------
             |               Robust
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0568883   .0183136    -3.11   0.002    -.0927929   -.0209838
      dA_mze |   .0312248   .0080189     3.89   0.000     .0155035    .0469461
             |
 rural_adult |
         L3. |  -.1355884   .0477743    -2.84   0.005    -.2292516   -.0419252
             |
       _cons |   .0428745   .0373721     1.15   0.251    -.0303948    .1161438
------------------------------------------------------------------------------

.         outreg2 using Tables\Table8, ctitle("Change in Log labor intensity (2006-1996)")  dec(3) nocons append
dir : seeout

. reg dlog_LA_TA                  dA_soy                  dA_mze  l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult     if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =    4.43
                                                       Prob > F      =  0.0002
                                                       R-squared     =  0.0069
                                                       Root MSE      =  .54929

------------------------------------------------------------------------------
             |               Robust
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0635392   .0205305    -3.09   0.002    -.1037901   -.0232884
      dA_mze |   .0329449   .0087442     3.77   0.000     .0158017    .0500882
             |
 rural_adult |
         L3. |  -.1767888   .0510959    -3.46   0.001    -.2769643   -.0766134
             |
  log_y_pc_r |
         L3. |   .0290896   .0388412     0.75   0.454    -.0470601    .1052393
             |
log_pop_area |
         L3. |  -.0169118    .010511    -1.61   0.108     -.037519    .0036954
             |
 alpha_adult |
         L3. |  -.1236139   .1158828    -1.07   0.286    -.3508065    .1035786
             |
       _cons |   .0794831   .1376234     0.58   0.564    -.1903327    .3492988
------------------------------------------------------------------------------

.         outreg2 using Tables\Table8, ctitle(" ")  dec(3) nocons append
dir : seeout

. reg dLa_L                       dA_soy                  dA_mze  l4.rural_adult                                          if year == 2010 &   rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  3,  4145) =  107.95
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0684
                                                       Root MSE      =  .07125

------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0176545   .0022884    -7.71   0.000    -.0221409   -.0131681
      dA_mze |    .004526    .001024     4.42   0.000     .0025185    .0065336
             |
 rural_adult |
         L4. |  -.0910122   .0051039   -17.83   0.000    -.1010186   -.0810058
             |
       _cons |  -.0036841    .003568    -1.03   0.302    -.0106793    .0033111
------------------------------------------------------------------------------

.         outreg2 using Tables\Table8, ctitle("Change in Employment share (2010-2000)")     dec(3) nocons append
dir : seeout

. reg dLa_L                       dA_soy                  dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 &    rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   59.24
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0726
                                                       Root MSE      =  .07111

------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0207294    .002388    -8.68   0.000    -.0254113   -.0160476
      dA_mze |   .0057186   .0010607     5.39   0.000     .0036391    .0077982
             |
 rural_adult |
         L4. |  -.0759097   .0068275   -11.12   0.000    -.0892953    -.062524
             |
  log_y_pc_r |
         L4. |   .0138749   .0042515     3.26   0.001     .0055396    .0222101
             |
log_pop_area |
         L4. |  -.0000517   .0009788    -0.05   0.958    -.0019707    .0018674
             |
 alpha_adult |
         L4. |  -.0122312    .013984    -0.87   0.382    -.0396473     .015185
             |
       _cons |  -.0625452   .0152813    -4.09   0.000    -.0925047   -.0325858
------------------------------------------------------------------------------

.         outreg2 using Tables\Table8, ctitle(" ")  dec(3) nocons append excel
Tables\Table8.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table  9: The effect of agricultural technological change on manufacturing                 ****
. ****           Employment share, employment and wages                                           ****
. ****************************************************************************************************
. reg dLm_L  dA_soy dA_mze l4.rural_adult                                         if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  3,  4145) =   85.92
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0628
                                                       Root MSE      =  .05508

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0234253   .0019246    12.17   0.000     .0196521    .0271985
      dA_mze |  -.0052995   .0008383    -6.32   0.000    -.0069431   -.0036559
             |
 rural_adult |
         L4. |  -.0061359   .0041698    -1.47   0.141    -.0143111    .0020392
             |
       _cons |  -.0095433   .0029743    -3.21   0.001    -.0153746    -.003712
------------------------------------------------------------------------------

.         outreg2 using Tables\Table9, ctitle("Change in Employment share (2010-2000)") dec(3) nocons replace
dir : seeout

. reg dLm_L  dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult   if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   53.34
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0728
                                                       Root MSE      =  .05481

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0210396   .0020852    10.09   0.000     .0169514    .0251277
      dA_mze |  -.0042137   .0009034    -4.66   0.000    -.0059849   -.0024425
             |
 rural_adult |
         L4. |   .0107396   .0051103     2.10   0.036     .0007206    .0207586
             |
  log_y_pc_r |
         L4. |   .0018562   .0031071     0.60   0.550    -.0042354    .0079478
             |
log_pop_area |
         L4. |   .0016603    .000708     2.35   0.019     .0002723    .0030484
             |
 alpha_adult |
         L4. |   .0341938   .0100351     3.41   0.001     .0145195    .0538681
             |
       _cons |  -.0545047   .0107549    -5.07   0.000    -.0755902   -.0334192
------------------------------------------------------------------------------

.         outreg2 using Tables\Table9, ctitle(" ")            dec(3) nocons append
dir : seeout

. reg dlog_Lm   dA_soy dA_mze l4.rural_adult                                      if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  3,  4145) =   91.52
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0558
                                                       Root MSE      =  .59065

------------------------------------------------------------------------------
             |               Robust
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .2183295   .0184045    11.86   0.000     .1822467    .2544123
      dA_mze |   -.057062   .0085387    -6.68   0.000    -.0738025   -.0403215
             |
 rural_adult |
         L4. |  -.1859177    .043971    -4.23   0.000    -.2721245   -.0997109
             |
       _cons |   .0866845   .0298257     2.91   0.004     .0282101     .145159
------------------------------------------------------------------------------

.         outreg2 using Tables\Table9, ctitle("Change in Log employment (2010-2000)")   dec(3) nocons append
dir : seeout

. reg dlog_Lm   dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   55.62
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0681
                                                       Root MSE      =  .58697

------------------------------------------------------------------------------
             |               Robust
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .185629   .0195433     9.50   0.000     .1473136    .2239444
      dA_mze |  -.0428767   .0089266    -4.80   0.000    -.0603776   -.0253757
             |
 rural_adult |
         L4. |   .0505559   .0563602     0.90   0.370    -.0599403    .1610521
             |
  log_y_pc_r |
         L4. |   .0931167   .0368444     2.53   0.012     .0208819    .1653515
             |
log_pop_area |
         L4. |   .0199595   .0081217     2.46   0.014     .0040366    .0358824
             |
 alpha_adult |
         L4. |   .1974533   .1174613     1.68   0.093     -.032834    .4277406
             |
       _cons |  -.6326233    .126915    -4.98   0.000     -.881445   -.3838017
------------------------------------------------------------------------------

.         outreg2 using Tables\Table9, ctitle(" ")                dec(3) nocons append
dir : seeout

. reg dlog_ym dA_soy dA_mze l4.rural_adult                                        if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  3,  4145) =   30.57
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0218
                                                       Root MSE      =  .36097

------------------------------------------------------------------------------
             |               Robust
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0316644   .0121012    -2.62   0.009    -.0553892   -.0079397
      dA_mze |   .0180932   .0053091     3.41   0.001     .0076844     .028502
             |
 rural_adult |
         L4. |   .1968707   .0256185     7.68   0.000     .1466447    .2470968
             |
       _cons |   .1980983   .0191121    10.37   0.000     .1606283    .2355683
------------------------------------------------------------------------------

.         outreg2 using Tables\Table9, ctitle("Change in Log wage (2010-2000)")         dec(3) nocons append
dir : seeout

. reg dlog_ym dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   33.12
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0448
                                                       Root MSE      =  .35682

------------------------------------------------------------------------------
             |               Robust
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0241096   .0124821    -1.93   0.053    -.0485812     .000362
      dA_mze |   .0139318   .0054323     2.56   0.010     .0032817    .0245819
             |
 rural_adult |
         L4. |  -.0144599   .0348133    -0.42   0.678    -.0827127    .0537929
             |
  log_y_pc_r |
         L4. |   -.107283   .0258166    -4.16   0.000    -.1578974   -.0566686
             |
log_pop_area |
         L4. |  -.0353255   .0047947    -7.37   0.000    -.0447257   -.0259253
             |
 alpha_adult |
         L4. |   .0929476   .0751947     1.24   0.216    -.0544744    .2403696
             |
       _cons |     .82554   .0896006     9.21   0.000     .6498747    1.001205
------------------------------------------------------------------------------

.         outreg2 using Tables\Table9, ctitle(" ")                                                        dec(3) nocons append excel
Tables\Table9.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table 10: The effect of agricultural technological change on employment shares             ****
. ****************************************************************************************************
. reg dLa_L dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   59.24
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0726
                                                       Root MSE      =  .07111

------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0207294    .002388    -8.68   0.000    -.0254113   -.0160476
      dA_mze |   .0057186   .0010607     5.39   0.000     .0036391    .0077982
             |
 rural_adult |
         L4. |  -.0759097   .0068275   -11.12   0.000    -.0892953    -.062524
             |
  log_y_pc_r |
         L4. |   .0138749   .0042515     3.26   0.001     .0055396    .0222101
             |
log_pop_area |
         L4. |  -.0000517   .0009788    -0.05   0.958    -.0019707    .0018674
             |
 alpha_adult |
         L4. |  -.0122312    .013984    -0.87   0.382    -.0396473     .015185
             |
       _cons |  -.0625452   .0152813    -4.09   0.000    -.0925047   -.0325858
------------------------------------------------------------------------------

.         outreg2 using Tables\Table10, ctitle("Change in Agriculture, Employment share (2010-2000)")         dec(3) nocons replace
dir : seeout

. reg dLm_L dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 & rf == 1  , r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   53.34
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0728
                                                       Root MSE      =  .05481

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0210396   .0020852    10.09   0.000     .0169514    .0251277
      dA_mze |  -.0042137   .0009034    -4.66   0.000    -.0059849   -.0024425
             |
 rural_adult |
         L4. |   .0107396   .0051103     2.10   0.036     .0007206    .0207586
             |
  log_y_pc_r |
         L4. |   .0018562   .0031071     0.60   0.550    -.0042354    .0079478
             |
log_pop_area |
         L4. |   .0016603    .000708     2.35   0.019     .0002723    .0030484
             |
 alpha_adult |
         L4. |   .0341938   .0100351     3.41   0.001     .0145195    .0538681
             |
       _cons |  -.0545047   .0107549    -5.07   0.000    -.0755902   -.0334192
------------------------------------------------------------------------------

.         outreg2 using Tables\Table10, ctitle("Change in Manufacturing, Employment share (2010-2000)")       dec(3) nocons append
dir : seeout

. reg dLs_L dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 & rf == 1  , r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   90.60
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1032
                                                       Root MSE      =  .05358

------------------------------------------------------------------------------
             |               Robust
       dLs_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0016721   .0017693    -0.95   0.345     -.005141    .0017967
      dA_mze |  -.0000446   .0008223    -0.05   0.957    -.0016567    .0015674
             |
 rural_adult |
         L4. |   .0425204    .005269     8.07   0.000     .0321904    .0528504
             |
  log_y_pc_r |
         L4. |  -.0149897   .0033601    -4.46   0.000    -.0215774   -.0084021
             |
log_pop_area |
         L4. |   .0002004   .0007063     0.28   0.777    -.0011844    .0015853
             |
 alpha_adult |
         L4. |  -.0086307   .0100947    -0.85   0.393    -.0284218    .0111603
             |
       _cons |    .089199   .0124425     7.17   0.000      .064805    .1135929
------------------------------------------------------------------------------

.         outreg2 using Tables\Table10, ctitle("Change in Services, Employment share (2010-2000)")                dec(3) nocons append
dir : seeout

. reg dLr_L dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   35.43
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0446
                                                       Root MSE      =  .03735

------------------------------------------------------------------------------
             |               Robust
       dLr_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .001362   .0011835     1.15   0.250    -.0009584    .0036824
      dA_mze |  -.0014602    .000553    -2.64   0.008    -.0025445    -.000376
             |
 rural_adult |
         L4. |   .0226497   .0036556     6.20   0.000     .0154827    .0298167
             |
  log_y_pc_r |
         L4. |  -.0007413   .0023376    -0.32   0.751    -.0053243    .0038417
             |
log_pop_area |
         L4. |  -.0018091   .0005594    -3.23   0.001    -.0029057   -.0007124
             |
 alpha_adult |
         L4. |  -.0133319   .0073098    -1.82   0.068     -.027663    .0009991
             |
       _cons |    .027851   .0087098     3.20   0.001     .0107751    .0449268
------------------------------------------------------------------------------

.         outreg2 using Tables\Table10, ctitle("Change in Other Sectors, Employment share (2010-2000)")       dec(3) nocons append excel
Tables\Table10.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table 11: Variable factor endowments                                                       ****
. ****************************************************************************************************
. reg migration_rate    dA_soy      dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 & rf == 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   80.45
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1040
                                                       Root MSE      =  .11755

------------------------------------------------------------------------------
             |               Robust
migration_~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0126594   .0038142    -3.32   0.001    -.0201372   -.0051815
      dA_mze |   .0056128   .0017357     3.23   0.001     .0022099    .0090158
             |
 rural_adult |
         L4. |  -.0780759    .011275    -6.92   0.000    -.1001809   -.0559709
             |
  log_y_pc_r |
         L4. |   .0507939    .007692     6.60   0.000     .0357134    .0658744
             |
log_pop_area |
         L4. |  -.0062276   .0020816    -2.99   0.003    -.0103086   -.0021465
             |
 alpha_adult |
         L4. |   .0094972   .0226385     0.42   0.675    -.0348865    .0538809
             |
       _cons |  -.1987965   .0250106    -7.95   0.000    -.2478307   -.1497624
------------------------------------------------------------------------------

.         outreg2 using Tables\Table11, ctitle("  , All") dec(3) nocons  replace
dir : seeout

. reg migration_rate    dA_soy      dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 & rf == 1 & frontier == 0, r

Linear regression                                      Number of obs =    2617
                                                       F(  6,  2610) =   61.44
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1187
                                                       Root MSE      =  .11589

------------------------------------------------------------------------------
             |               Robust
migration_~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0147391   .0048994    -3.01   0.003    -.0243463    -.005132
      dA_mze |   .0072198   .0023374     3.09   0.002     .0026365    .0118031
             |
 rural_adult |
         L4. |  -.0950523   .0135479    -7.02   0.000    -.1216181   -.0684866
             |
  log_y_pc_r |
         L4. |   .0496259   .0086828     5.72   0.000     .0325999    .0666519
             |
log_pop_area |
         L4. |  -.0016019   .0025448    -0.63   0.529    -.0065918    .0033881
             |
 alpha_adult |
         L4. |   .0184309   .0267778     0.69   0.491     -.034077    .0709388
             |
       _cons |  -.2174861   .0296858    -7.33   0.000    -.2756962    -.159276
------------------------------------------------------------------------------

.         outreg2 using Tables\Table11, ctitle("Migration rate (2010-2000), Non-Frontier") dec(3) nocons  append
dir : seeout

. reg migration_rate    dA_soy      dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 & rf == 1 & frontier == 1, r

Linear regression                                      Number of obs =    1532
                                                       F(  6,  1525) =   30.85
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1129
                                                       Root MSE      =  .11783

------------------------------------------------------------------------------
             |               Robust
migration_~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0122131   .0060747    -2.01   0.045    -.0241288   -.0002974
      dA_mze |    .002874   .0025455     1.13   0.259    -.0021191     .007867
             |
 rural_adult |
         L4. |  -.0348888   .0202751    -1.72   0.085    -.0746589    .0048813
             |
  log_y_pc_r |
         L4. |   .0465974   .0132646     3.51   0.000     .0205785    .0726163
             |
log_pop_area |
         L4. |  -.0094812   .0031366    -3.02   0.003    -.0156336   -.0033287
             |
 alpha_adult |
         L4. |   .0786693   .0381026     2.06   0.039     .0039302    .1534083
             |
       _cons |  -.2134285   .0443832    -4.81   0.000    -.3004871   -.1263699
------------------------------------------------------------------------------

.         outreg2 using Tables\Table11, ctitle(" , Frontier") dec(3) nocons  append
dir : seeout

. reg dLa_L dA_soy      dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 & rf == 1 & frontier == 0, r

Linear regression                                      Number of obs =    2617
                                                       F(  6,  2610) =   45.74
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0801
                                                       Root MSE      =  .06709

------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0226953   .0028721    -7.90   0.000    -.0283271   -.0170635
      dA_mze |   .0076303   .0013233     5.77   0.000     .0050355     .010225
             |
 rural_adult |
         L4. |    -.08095   .0081892    -9.89   0.000    -.0970079   -.0648921
             |
  log_y_pc_r |
         L4. |   .0169152     .00538     3.14   0.002     .0063657    .0274647
             |
log_pop_area |
         L4. |  -.0009425   .0012638    -0.75   0.456    -.0034208    .0015357
             |
 alpha_adult |
         L4. |  -.0258953    .017361    -1.49   0.136     -.059938    .0081474
             |
       _cons |  -.0664273   .0197683    -3.36   0.001    -.1051904   -.0276642
------------------------------------------------------------------------------

.         outreg2 using Tables\Table11, ctitle("Change in Agri Empl. Share (2010-2000), Non-Frontier") dec(3) nocons  append
dir : seeout

. reg dLa_L dA_soy      dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 & rf == 1 & frontier == 1, r

Linear regression                                      Number of obs =    1532
                                                       F(  6,  1525) =   26.10
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0759
                                                       Root MSE      =  .07698

------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0196765   .0041918    -4.69   0.000    -.0278989   -.0114542
      dA_mze |   .0026205   .0017653     1.48   0.138    -.0008422    .0060833
             |
 rural_adult |
         L4. |  -.0613703   .0123008    -4.99   0.000    -.0854986    -.037242
             |
  log_y_pc_r |
         L4. |   .0079037   .0067647     1.17   0.243    -.0053654    .0211727
             |
log_pop_area |
         L4. |   .0010033   .0015075     0.67   0.506    -.0019537    .0039603
             |
 alpha_adult |
         L4. |   .0315476   .0236701     1.33   0.183    -.0148817    .0779769
             |
       _cons |  -.0627055   .0241159    -2.60   0.009    -.1100093   -.0154017
------------------------------------------------------------------------------

.         outreg2 using Tables\Table11, ctitle(" , Frontier") dec(3) nocons  append
dir : seeout

. reg dLm_L dA_soy      dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 & rf == 1 & frontier == 0, r

Linear regression                                      Number of obs =    2617
                                                       F(  6,  2610) =   36.60
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0765
                                                       Root MSE      =  .05217

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .022786   .0024407     9.34   0.000     .0180001    .0275719
      dA_mze |  -.0051608   .0011116    -4.64   0.000    -.0073405    -.002981
             |
 rural_adult |
         L4. |   .0187459   .0060085     3.12   0.002      .006964    .0305279
             |
  log_y_pc_r |
         L4. |   .0062074   .0038097     1.63   0.103     -.001263    .0136778
             |
log_pop_area |
         L4. |   .0014617   .0009107     1.60   0.109    -.0003241    .0032474
             |
 alpha_adult |
         L4. |   .0182361   .0124543     1.46   0.143    -.0061852    .0426573
             |
       _cons |  -.0634631   .0133519    -4.75   0.000    -.0896444   -.0372817
------------------------------------------------------------------------------

.         outreg2 using Tables\Table11, ctitle("Change in Manuf Empl. Share (2010-2000), Non-Frontier") dec(3) nocons  append
dir : seeout

. reg dLm_L dA_soy      dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 & rf == 1 & frontier == 1, r

Linear regression                                      Number of obs =    1532
                                                       F(  6,  1525) =   15.65
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0659
                                                       Root MSE      =  .05878

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0194125   .0038113     5.09   0.000     .0119364    .0268885
      dA_mze |  -.0029041   .0015388    -1.89   0.059    -.0059225    .0001143
             |
 rural_adult |
         L4. |  -.0040033   .0094897    -0.42   0.673    -.0226175     .014611
             |
  log_y_pc_r |
         L4. |   -.002857   .0051996    -0.55   0.583    -.0130561    .0073421
             |
log_pop_area |
         L4. |   .0014274   .0010622     1.34   0.179    -.0006561    .0035109
             |
 alpha_adult |
         L4. |   .0376082    .016349     2.30   0.022     .0055392    .0696772
             |
       _cons |  -.0337306   .0186181    -1.81   0.070    -.0702504    .0027892
------------------------------------------------------------------------------

.         outreg2 using Tables\Table11, ctitle(" , Frontier") dec(3) nocons  append excel
Tables\Table11.xml
dir : seeout

.         
. ****************************************************************************************************
. ****                           Robustness results in appendix                                   ****
. ****��������������������������������������������������������������������������������������������****
. **** Table  A1: The effect of agricultural technological change on employment shares            ****
. ****            Observations are weighted by share of aggregate employment                      ****
. ****************************************************************************************************
. reg dlog_PQ_LA     dA_soy dA_mze  l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year == 2006 & f.rf == 1 [pw=f.weight], r
(sum of wgt is   1.0000e+00)

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =    3.80
                                                       Prob > F      =  0.0009
                                                       R-squared     =  0.0362
                                                       Root MSE      =  .77634

------------------------------------------------------------------------------
             |               Robust
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .2222631   .0940151     2.36   0.018     .0379432    .4065831
      dA_mze |  -.0990783   .0396812    -2.50   0.013    -.1768748   -.0212819
             |
 rural_adult |
         L3. |   .1606062   .2350912     0.68   0.495    -.3002987    .6215111
             |
  log_y_pc_r |
         L3. |  -.1846457   .2125481    -0.87   0.385     -.601354    .2320627
             |
log_pop_area |
         L3. |  -.0015069   .0366942    -0.04   0.967    -.0734472    .0704333
             |
 alpha_adult |
         L3. |  -.0042376   .5391366    -0.01   0.994    -1.061235    1.052759
             |
       _cons |   1.273302   .6534639     1.95   0.051    -.0078376    2.554443
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA1, ctitle("Change in log output per, worker (2006-1996)")            dec(3) nocons replace
dir : seeout

. reg dLa_L          dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 &   rf == 1 [pw=weight], r
(sum of wgt is   1.0000e+00)

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =  198.89
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.3306
                                                       Root MSE      =    .042

------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0108013   .0016347    -6.61   0.000    -.0140063   -.0075963
      dA_mze |   .0030612   .0006183     4.95   0.000     .0018491    .0042733
             |
 rural_adult |
         L4. |  -.0782668   .0078605    -9.96   0.000    -.0936775    -.062856
             |
  log_y_pc_r |
         L4. |   .0057986   .0025103     2.31   0.021     .0008771      .01072
             |
log_pop_area |
         L4. |   .0032274   .0005833     5.53   0.000     .0020838     .004371
             |
 alpha_adult |
         L4. |   .0272658   .0138157     1.97   0.049     .0001796    .0543519
             |
       _cons |  -.0689061   .0111697    -6.17   0.000    -.0908048   -.0470075
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA1, ctitle("Change in Agriculture, Employment share (2010-2000)")     dec(3) nocons append
dir : seeout

. reg dLm_L          dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 &   rf == 1 [pw=weight], r
(sum of wgt is   1.0000e+00)

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   16.10
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1176
                                                       Root MSE      =   .0369

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0105111   .0022875     4.60   0.000     .0060264    .0149958
      dA_mze |  -.0010475   .0010478    -1.00   0.318    -.0031018    .0010068
             |
 rural_adult |
         L4. |  -.0061477   .0075309    -0.82   0.414    -.0209124     .008617
             |
  log_y_pc_r |
         L4. |  -.0156239   .0045452    -3.44   0.001     -.024535   -.0067128
             |
log_pop_area |
         L4. |  -.0041956   .0007845    -5.35   0.000    -.0057337   -.0026576
             |
 alpha_adult |
         L4. |    .044312   .0144403     3.07   0.002     .0160013    .0726227
             |
       _cons |   .0436781   .0160485     2.72   0.007     .0122145    .0751417
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA1, ctitle("Change in Manufacturing, Employment share (2010-2000)")   dec(3) nocons append
dir : seeout

. reg dLs_L          dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 &   rf == 1 [pw=weight], r
(sum of wgt is   1.0000e+00)

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   76.20
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1623
                                                       Root MSE      =  .03467

------------------------------------------------------------------------------
             |               Robust
       dLs_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0005967   .0020341     0.29   0.769    -.0033911    .0045845
      dA_mze |  -.0015015   .0008941    -1.68   0.093    -.0032544    .0002514
             |
 rural_adult |
         L4. |   .0669877   .0073554     9.11   0.000     .0525672    .0814082
             |
  log_y_pc_r |
         L4. |   .0084925     .00456     1.86   0.063    -.0004475    .0174325
             |
log_pop_area |
         L4. |   .0019891   .0007498     2.65   0.008      .000519    .0034592
             |
 alpha_adult |
         L4. |  -.0515515   .0147214    -3.50   0.000    -.0804133   -.0226897
             |
       _cons |   .0023773   .0168283     0.14   0.888    -.0306152    .0353699
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA1, ctitle("Change in Services, Employment share (2010-2000)")            dec(3) nocons append
dir : seeout

. reg dLr_L          dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year == 2010 &   rf == 1 [pw=weight], r
(sum of wgt is   1.0000e+00)

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   39.59
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1115
                                                       Root MSE      =    .022

------------------------------------------------------------------------------
             |               Robust
       dLr_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0003065   .0014141    -0.22   0.828     -.003079    .0024659
      dA_mze |  -.0005122   .0006462    -0.79   0.428    -.0017791    .0007547
             |
 rural_adult |
         L4. |   .0174267    .004951     3.52   0.000       .00772    .0271334
             |
  log_y_pc_r |
         L4. |   .0013328   .0020667     0.64   0.519    -.0027191    .0053847
             |
log_pop_area |
         L4. |  -.0010209   .0004692    -2.18   0.030    -.0019408   -.0001009
             |
 alpha_adult |
         L4. |  -.0200263   .0076874    -2.61   0.009    -.0350977   -.0049549
             |
       _cons |   .0228507   .0081849     2.79   0.005      .006804    .0388975
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA1, ctitle("Change in Other Sectors, Employment share (2010-2000)")   dec(3) nocons append excel
Tables\TableA1.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table  A2: Basic correlation in the data                                                   ****
. ****            Income per Capita and Services Employment Share                                 ****
. ****************************************************************************************************
. reg dlog_y                     l.dsoy_TA_w   l.dmze_TA_w               if year == 2010 & ols==1, r

Linear regression                                      Number of obs =    3765
                                                       F(  2,  3762) =    3.69
                                                       Prob > F      =  0.0251
                                                       R-squared     =  0.0024
                                                       Root MSE      =  .16847

------------------------------------------------------------------------------
             |               Robust
      dlog_y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   dsoy_TA_w |
         L1. |   .1204186   .0744888     1.62   0.106    -.0256237    .2664609
             |
   dmze_TA_w |
         L1. |   .0797363   .0431027     1.85   0.064    -.0047707    .1642433
             |
       _cons |   .1446825   .0028324    51.08   0.000     .1391294    .1502357
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA2, ctitle("Change in income, per capita (2010-2000)")         dec( 3 ) nocons replace
dir : seeout

. reg dLs_L                      l.dsoy_TA_w   l.dmze_TA_w               if year == 2010 & ols==1, r

Linear regression                                      Number of obs =    3765
                                                       F(  2,  3762) =    3.24
                                                       Prob > F      =  0.0393
                                                       R-squared     =  0.0017
                                                       Root MSE      =  .05605

------------------------------------------------------------------------------
             |               Robust
       dLs_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   dsoy_TA_w |
         L1. |   .0065932   .0187429     0.35   0.725     -.030154    .0433404
             |
   dmze_TA_w |
         L1. |   .0330529   .0150111     2.20   0.028     .0036222    .0624836
             |
       _cons |   .0331686   .0009626    34.46   0.000     .0312814    .0350558
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA2, ctitle("Change in Services, Employment share (2010-2000)") dec( 3 ) nocons append
dir : seeout

. reg dlog_y l.dsoy_TA_w_FamSh   l.dsoy_TA_w   l.dmze_TA_w   l.soy_FamSh if year == 2010 & ols==1, r

Linear regression                                      Number of obs =    3765
                                                       F(  4,  3760) =    4.88
                                                       Prob > F      =  0.0006
                                                       R-squared     =  0.0080
                                                       Root MSE      =  .16804

---------------------------------------------------------------------------------
                |               Robust
         dlog_y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
dsoy_TA_w_FamSh |
            L1. |   .9943208   .3219138     3.09   0.002     .3631782    1.625463
                |
      dsoy_TA_w |
            L1. |  -.5404203   .2262489    -2.39   0.017    -.9840027   -.0968379
                |
      dmze_TA_w |
            L1. |   .0740525   .0428132     1.73   0.084    -.0098868    .1579918
                |
      soy_FamSh |
            L1. |   .0053733   .0117946     0.46   0.649    -.0177511    .0284976
                |
          _cons |   .1449995   .0030626    47.35   0.000      .138995     .151004
---------------------------------------------------------------------------------

.         outreg2 using Tables\TableA2, ctitle("Change in income, per capita (2010-2000)")         dec( 3 ) nocons append
dir : seeout

. reg dLs_L  l.dsoy_TA_w_FamSh   l.dsoy_TA_w   l.dmze_TA_w   l.soy_FamSh if year == 2010 & ols==1, r

Linear regression                                      Number of obs =    3765
                                                       F(  4,  3760) =    5.09
                                                       Prob > F      =  0.0004
                                                       R-squared     =  0.0042
                                                       Root MSE      =  .05599

---------------------------------------------------------------------------------
                |               Robust
          dLs_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------+----------------------------------------------------------------
dsoy_TA_w_FamSh |
            L1. |   .1340064    .068731     1.95   0.051    -.0007473    .2687601
                |
      dsoy_TA_w |
            L1. |  -.1047383   .0501364    -2.09   0.037    -.2030354   -.0064411
                |
      dmze_TA_w |
            L1. |   .0328772   .0150113     2.19   0.029     .0034462    .0623081
                |
      soy_FamSh |
            L1. |   .0079242   .0034111     2.32   0.020     .0012364     .014612
                |
          _cons |   .0325565   .0010587    30.75   0.000     .0304809    .0346321
---------------------------------------------------------------------------------

.         outreg2 using Tables\TableA2, ctitle("Change in Services, Employment share (2010-2000)") dec( 3 ) nocons append excel sortvar(l.dsoy_TA_w_FamSh l.dsoy_TA_w   l
> .dmze_TA_w   l.soy_FamSh)
Tables\TableA2.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table  A3: The Effect of Agricultural Technical Change on                                  ****
. ****            Income per Capita and Services Employment Share                                 ****
. ****************************************************************************************************
. reg dlog_y                dA_soy dA_mze             l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf==1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   79.58
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0929
                                                       Root MSE      =  .16058

------------------------------------------------------------------------------
             |               Robust
      dlog_y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0179099   .0053271     3.36   0.001     .0074659    .0283538
      dA_mze |   .0008882   .0023142     0.38   0.701    -.0036489    .0054253
             |
 rural_adult |
         L4. |   .0369473   .0167152     2.21   0.027     .0041764    .0697181
             |
  log_y_pc_r |
         L4. |   -.109713   .0095463   -11.49   0.000    -.1284289   -.0909971
             |
log_pop_area |
         L4. |   .0101432   .0022169     4.58   0.000     .0057969    .0144895
             |
 alpha_adult |
         L4. |   .1103828   .0300161     3.68   0.000      .051535    .1692305
             |
       _cons |   .4787338   .0366269    13.07   0.000     .4069254    .5505423
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA3, ctitle("Change in income, per capita (2010-2000)")         dec( 3 ) nocons replace
dir : seeout

. reg dLs_L                 dA_soy dA_mze             l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf==1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   90.60
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1032
                                                       Root MSE      =  .05358

------------------------------------------------------------------------------
             |               Robust
       dLs_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0016721   .0017693    -0.95   0.345     -.005141    .0017967
      dA_mze |  -.0000446   .0008223    -0.05   0.957    -.0016567    .0015674
             |
 rural_adult |
         L4. |   .0425204    .005269     8.07   0.000     .0321904    .0528504
             |
  log_y_pc_r |
         L4. |  -.0149897   .0033601    -4.46   0.000    -.0215774   -.0084021
             |
log_pop_area |
         L4. |   .0002004   .0007063     0.28   0.777    -.0011844    .0015853
             |
 alpha_adult |
         L4. |  -.0086307   .0100947    -0.85   0.393    -.0284218    .0111603
             |
       _cons |    .089199   .0124425     7.17   0.000      .064805    .1135929
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA3, ctitle("Change in Services, Employment share (2010-2000)") dec( 3 ) nocons append
dir : seeout

. reg dlog_y l.dA_soy_FamSh dA_soy dA_mze l.soy_FamSh l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf==1, r

Linear regression                                      Number of obs =    4149
                                                       F(  8,  4140) =   62.49
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0978
                                                       Root MSE      =  .16019

------------------------------------------------------------------------------
             |               Robust
      dlog_y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dA_soy_FamSh |
         L1. |   .0189976   .0100236     1.90   0.058     -.000654    .0386493
             |
      dA_soy |   .0126008   .0056816     2.22   0.027     .0014619    .0237397
      dA_mze |   .0000864   .0023117     0.04   0.970    -.0044457    .0046186
             |
   soy_FamSh |
         L1. |   .0016222   .0231945     0.07   0.944    -.0438515    .0470959
             |
 rural_adult |
         L4. |   .0181417   .0169421     1.07   0.284     -.015074    .0513573
             |
  log_y_pc_r |
         L4. |  -.1096987   .0095053   -11.54   0.000    -.1283343   -.0910632
             |
log_pop_area |
         L4. |   .0094238   .0022046     4.27   0.000     .0051015     .013746
             |
 alpha_adult |
         L4. |   .0793551   .0310888     2.55   0.011     .0184044    .1403058
             |
       _cons |   .5176226   .0366672    14.12   0.000     .4457351      .58951
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA3, ctitle("Change in income, per capita (2010-2000)")         dec( 3 ) nocons append
dir : seeout

. reg dLs_L  l.dA_soy_FamSh dA_soy dA_mze l.soy_FamSh l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf==1, r

Linear regression                                      Number of obs =    4149
                                                       F(  8,  4140) =   72.75
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1095
                                                       Root MSE      =   .0534

------------------------------------------------------------------------------
             |               Robust
       dLs_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dA_soy_FamSh |
         L1. |  -.0011711   .0029067    -0.40   0.687    -.0068698    .0045275
             |
      dA_soy |   -.002092   .0018723    -1.12   0.264    -.0057627    .0015786
      dA_mze |  -.0005035   .0008252    -0.61   0.542    -.0021213    .0011143
             |
   soy_FamSh |
         L1. |   .0193254   .0064679     2.99   0.003     .0066448     .032006
             |
 rural_adult |
         L4. |   .0342384   .0055047     6.22   0.000     .0234463    .0450306
             |
  log_y_pc_r |
         L4. |  -.0153146   .0033624    -4.55   0.000    -.0219068   -.0087224
             |
log_pop_area |
         L4. |  -.0001099   .0007098    -0.15   0.877    -.0015016    .0012817
             |
 alpha_adult |
         L4. |  -.0216441   .0104399    -2.07   0.038    -.0421119   -.0011763
             |
       _cons |    .104558   .0127512     8.20   0.000     .0795589    .1295571
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA3, ctitle("Change in Services, Employment share (2010-2000)") dec( 3 ) nocons append excel sortvar(l.dA_soy_FamSh dA_soy dA_mze l.so
> y_FamSh)
Tables\TableA3.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table  A4: The effect of technological change on agriculture                               ****
. ****            Robustness to controlling for additional initial municipality characteristics   ****
. ****            Panel A.                                                                        ****
. ****************************************************************************************************
. reg dsoy_TA_w      dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult l4.log_PQ_LA                                   if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    3841
                                                       F(  7,  3833) =   44.14
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1177
                                                       Root MSE      =  .04048

------------------------------------------------------------------------------
             |               Robust
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0122937   .0018739     6.56   0.000     .0086198    .0159676
      dA_mze |  -.0003818   .0007407    -0.52   0.606     -.001834    .0010704
             |
 rural_adult |
         L3. |   .0386065   .0046847     8.24   0.000     .0294218    .0477911
             |
  log_y_pc_r |
         L3. |  -.0016533   .0024346    -0.68   0.497    -.0064265      .00312
             |
log_pop_area |
         L3. |  -.0017928   .0004831    -3.71   0.000    -.0027399   -.0008457
             |
 alpha_adult |
         L3. |   .0674292   .0067403    10.00   0.000     .0542143    .0806442
             |
   log_PQ_LA |
         L4. |   .0007048   .0008796     0.80   0.423    -.0010197    .0024294
             |
       _cons |  -.0627643   .0101855    -6.16   0.000    -.0827338   -.0427948
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelA, ctitle(" ")                                       dec(3) nocons replace
dir : seeout

. reg dsoy_TA_w      dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult              l3.log_ya_sct91               if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    3683
                                                       F(  7,  3675) =   42.45
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1173
                                                       Root MSE      =  .04042

------------------------------------------------------------------------------
             |               Robust
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .013367   .0018245     7.33   0.000     .0097899    .0169442
      dA_mze |  -.0007424   .0007152    -1.04   0.299    -.0021447    .0006599
             |
 rural_adult |
         L3. |   .0369428   .0047542     7.77   0.000     .0276217    .0462639
             |
  log_y_pc_r |
         L3. |   .0000745   .0026944     0.03   0.978    -.0052082    .0053572
             |
log_pop_area |
         L3. |  -.0021095   .0004986    -4.23   0.000    -.0030872   -.0011319
             |
 alpha_adult |
         L3. |   .0668529   .0071025     9.41   0.000     .0529276    .0807781
             |
log_ya_sct91 |
         L3. |  -.0034891   .0016023    -2.18   0.030    -.0066306   -.0003476
             |
       _cons |  -.0501604    .009996    -5.02   0.000    -.0697587   -.0305621
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelA, ctitle("Change in Soy area, share (2006-1996)")   dec(3) nocons append
dir : seeout

. reg dsoy_TA_w      dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult                            l3.La_L_sct91   if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    3841
                                                       F(  7,  3833) =   44.34
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1209
                                                       Root MSE      =  .04041

------------------------------------------------------------------------------
             |               Robust
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |      .0114   .0017706     6.44   0.000     .0079286    .0148715
      dA_mze |  -.0001238   .0007067    -0.18   0.861    -.0015093    .0012617
             |
 rural_adult |
         L3. |   .0238458   .0052464     4.55   0.000     .0135598    .0341318
             |
  log_y_pc_r |
         L3. |   .0008094   .0022785     0.36   0.722    -.0036578    .0052765
             |
log_pop_area |
         L3. |  -.0012163   .0005423    -2.24   0.025    -.0022796    -.000153
             |
 alpha_adult |
         L3. |   .0695405   .0068821    10.10   0.000     .0560476    .0830334
             |
  La_L_sct91 |
         L3. |   .0234748   .0058827     3.99   0.000     .0119413    .0350083
             |
       _cons |  -.0792979   .0109507    -7.24   0.000    -.1007676   -.0578282
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelA, ctitle(" ")                                       dec(3) nocons append
dir : seeout

. reg dmze_TA_w      dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult l4.log_PQ_LA                                   if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    4032
                                                       F(  7,  4024) =    8.84
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0149
                                                       Root MSE      =  .06663

------------------------------------------------------------------------------
             |               Robust
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0004409      .0026    -0.17   0.865    -.0055383    .0046565
      dA_mze |   .0032287   .0010233     3.16   0.002     .0012225    .0052349
             |
 rural_adult |
         L3. |   .0139787   .0063875     2.19   0.029     .0014556    .0265018
             |
  log_y_pc_r |
         L3. |  -.0032847   .0039143    -0.84   0.401     -.010959    .0043895
             |
log_pop_area |
         L3. |   .0035481   .0006424     5.52   0.000     .0022886    .0048075
             |
 alpha_adult |
         L3. |  -.0034669   .0119533    -0.29   0.772    -.0269019    .0199681
             |
   log_PQ_LA |
         L4. |  -.0009866   .0015122    -0.65   0.514    -.0039514    .0019781
             |
       _cons |   .0008681   .0146526     0.06   0.953    -.0278592    .0295954
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelA, ctitle(" ")                                       dec(3) nocons append
dir : seeout

. reg dmze_TA_w      dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult              l3.log_ya_sct91               if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    3872
                                                       F(  7,  3864) =    8.06
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0147
                                                       Root MSE      =  .06537

------------------------------------------------------------------------------
             |               Robust
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0005246   .0025488     0.21   0.837    -.0044726    .0055218
      dA_mze |   .0025808   .0010219     2.53   0.012     .0005773    .0045843
             |
 rural_adult |
         L3. |   .0118171   .0062645     1.89   0.059     -.000465    .0240992
             |
  log_y_pc_r |
         L3. |   .0002306   .0040971     0.06   0.955    -.0078021    .0082633
             |
log_pop_area |
         L3. |   .0028441   .0006465     4.40   0.000     .0015766    .0041117
             |
 alpha_adult |
         L3. |  -.0072075   .0124765    -0.58   0.564    -.0316686    .0172536
             |
log_ya_sct91 |
         L3. |  -.0084022   .0026478    -3.17   0.002    -.0135934   -.0032111
             |
       _cons |   .0313217   .0165711     1.89   0.059    -.0011673    .0638106
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelA, ctitle("Change in Maize area, share (2006-1996)") dec(3) nocons append
dir : seeout

. reg dmze_TA_w      dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult                            l3.La_L_sct91   if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    4032
                                                       F(  7,  4024) =    8.97
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0152
                                                       Root MSE      =  .06662

------------------------------------------------------------------------------
             |               Robust
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0017017   .0025103    -0.68   0.498    -.0066233    .0032199
      dA_mze |   .0036193   .0010176     3.56   0.000     .0016243    .0056143
             |
 rural_adult |
         L3. |   .0067332   .0070291     0.96   0.338    -.0070478    .0205141
             |
  log_y_pc_r |
         L3. |   -.003088      .0038    -0.81   0.416    -.0105381    .0043622
             |
log_pop_area |
         L3. |   .0038451   .0007102     5.41   0.000     .0024527    .0052375
             |
 alpha_adult |
         L3. |  -.0040088   .0121134    -0.33   0.741    -.0277577    .0197402
             |
  La_L_sct91 |
         L3. |    .011977   .0089627     1.34   0.182    -.0055949    .0295488
             |
       _cons |  -.0037561   .0164201    -0.23   0.819    -.0359485    .0284363
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelA, ctitle(" ")                                       dec(3) nocons append excel sortvar(dA_soy dA_mze l4.log_PQ_LA l3.log_ya_
> sct91 l3.La_L_sct91)
Tables\TableA4_PanelA.xml
dir : seeout

. 
.         
. ****************************************************************************************************
. ****            Panel B.                                                                        ****
. ****************************************************************************************************
. reg dlog_PQ_LA     dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult l4.log_PQ_LA                               if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  7,  4141) =    8.35
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0145
                                                       Root MSE      =  .80534

------------------------------------------------------------------------------
             |               Robust
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1722985   .0293651     5.87   0.000     .1147271    .2298698
      dA_mze |  -.0448642   .0125501    -3.57   0.000    -.0694692   -.0202592
             |
 rural_adult |
         L3. |   .1212079   .0750569     1.61   0.106    -.0259439    .2683596
             |
  log_y_pc_r |
         L3. |   .0356975   .0507858     0.70   0.482    -.0638699    .1352649
             |
log_pop_area |
         L3. |  -.0160516   .0124153    -1.29   0.196    -.0403923    .0082891
             |
 alpha_adult |
         L3. |  -.2167236    .149874    -1.45   0.148    -.5105572    .0771099
             |
   log_PQ_LA |
         L4. |  -.0629715   .0218051    -2.89   0.004    -.1057213   -.0202217
             |
       _cons |   .5000806   .1889242     2.65   0.008     .1296877    .8704736
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelB, ctitle(" ")                                          dec(3) nocons replace
dir : seeout

. reg dlog_PQ_LA     dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult              l3.log_ya_sct91               if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    3985
                                                       F(  7,  3977) =    6.68
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0128
                                                       Root MSE      =  .80585

------------------------------------------------------------------------------
             |               Robust
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1321493   .0275632     4.79   0.000     .0781099    .1861887
      dA_mze |  -.0341193   .0121512    -2.81   0.005    -.0579425   -.0102962
             |
 rural_adult |
         L3. |    .138519   .0772138     1.79   0.073    -.0128634    .2899014
             |
  log_y_pc_r |
         L3. |  -.0038093   .0521426    -0.07   0.942     -.106038    .0984194
             |
log_pop_area |
         L3. |  -.0238296   .0128235    -1.86   0.063    -.0489709    .0013117
             |
 alpha_adult |
         L3. |  -.2920992    .150042    -1.95   0.052    -.5862658    .0020673
             |
log_ya_sct91 |
         L3. |  -.0121181   .0362623    -0.33   0.738    -.0832126    .0589764
             |
       _cons |   .7308687   .2157693     3.39   0.001       .30784    1.153897
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelB, ctitle("Change in Log value per worker (2006-1996)") dec(3) nocons append  
dir : seeout

. reg dlog_PQ_LA     dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult                            l3.La_L_sct91   if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  7,  4141) =    6.79
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0123
                                                       Root MSE      =  .80621

------------------------------------------------------------------------------
             |               Robust
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1279846   .0284536     4.50   0.000     .0722003    .1837689
      dA_mze |  -.0311568   .0123337    -2.53   0.012    -.0553374   -.0069762
             |
 rural_adult |
         L3. |   .0640103    .115385     0.55   0.579    -.1622063    .2902269
             |
  log_y_pc_r |
         L3. |  -.0054283    .049412    -0.11   0.913    -.1023023    .0914457
             |
log_pop_area |
         L3. |  -.0132776   .0122274    -1.09   0.278    -.0372499    .0106948
             |
 alpha_adult |
         L3. |  -.2832844   .1462994    -1.94   0.053    -.5701097     .003541
             |
  La_L_sct91 |
         L3. |   .1127157   .1274195     0.88   0.376    -.1370949    .3625263
             |
       _cons |   .6122614   .1930548     3.17   0.002     .2337703    .9907525
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelB, ctitle(" ")                                          dec(3) nocons append
dir : seeout

. reg dlog_LA_TA     dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult l4.log_PQ_LA                               if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  7,  4141) =    4.51
                                                       Prob > F      =  0.0001
                                                       R-squared     =  0.0098
                                                       Root MSE      =  .54853

------------------------------------------------------------------------------
             |               Robust
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0923112   .0222188    -4.15   0.000     -.135872   -.0487505
      dA_mze |    .041874    .009379     4.46   0.000      .023486    .0602619
             |
 rural_adult |
         L3. |  -.1672011   .0509267    -3.28   0.001    -.2670449   -.0673574
             |
  log_y_pc_r |
         L3. |  -.0091439   .0419022    -0.22   0.827    -.0912946    .0730069
             |
log_pop_area |
         L3. |  -.0169642   .0105544    -1.61   0.108    -.0376564    .0037281
             |
 alpha_adult |
         L3. |  -.1792256   .1189703    -1.51   0.132    -.4124712    .0540201
             |
   log_PQ_LA |
         L4. |   .0478948   .0179164     2.67   0.008     .0127691    .0830205
             |
       _cons |   .2184104   .1518315     1.44   0.150    -.0792609    .5160817
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelB, ctitle(" ")                                          dec(3) nocons append
dir : seeout

. reg dlog_LA_TA     dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult              l3.log_ya_sct91               if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    3985
                                                       F(  7,  3977) =    4.45
                                                       Prob > F      =  0.0001
                                                       R-squared     =  0.0084
                                                       Root MSE      =  .54609

------------------------------------------------------------------------------
             |               Robust
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0611265   .0209693    -2.92   0.004     -.102238   -.0200149
      dA_mze |   .0306745    .008781     3.49   0.000     .0134589    .0478901
             |
 rural_adult |
         L3. |  -.1742093   .0527969    -3.30   0.001    -.2777208   -.0706978
             |
  log_y_pc_r |
         L3. |   .0593523   .0398787     1.49   0.137    -.0188323     .137537
             |
log_pop_area |
         L3. |  -.0252558   .0108578    -2.33   0.020    -.0465433   -.0039684
             |
 alpha_adult |
         L3. |  -.1703252   .1182613    -1.44   0.150    -.4021836    .0615332
             |
log_ya_sct91 |
         L3. |  -.0407847   .0283517    -1.44   0.150      -.09637    .0148006
             |
       _cons |   .2155972   .1720697     1.25   0.210    -.1217559    .5529504
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelB, ctitle("Change in Log labor intensity (2006-1996)")  dec(3) nocons append
dir : seeout

. reg dlog_LA_TA     dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult                            l3.La_L_sct91   if year == 2006 & rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  7,  4141) =    3.81
                                                       Prob > F      =  0.0004
                                                       R-squared     =  0.0072
                                                       Root MSE      =  .54926

------------------------------------------------------------------------------
             |               Robust
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    -.06903   .0213027    -3.24   0.001    -.1107947   -.0272653
      dA_mze |    .034611   .0089847     3.85   0.000     .0169961    .0522259
             |
 rural_adult |
         L3. |   -.235892   .0796829    -2.96   0.003    -.3921133   -.0796708
             |
  log_y_pc_r |
         L3. |   .0368312   .0400032     0.92   0.357    -.0415965     .115259
             |
log_pop_area |
         L3. |  -.0145047   .0106909    -1.36   0.175    -.0354646    .0064552
             |
 alpha_adult |
         L3. |  -.1180622   .1155509    -1.02   0.307     -.344604    .1084795
             |
  La_L_sct91 |
         L3. |   .0954376   .0903204     1.06   0.291    -.0816389     .272514
             |
       _cons |   .0198077   .1498814     0.13   0.895    -.2740403    .3136558
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelB, ctitle(" ")                                          dec(3) nocons append
dir : seeout

. reg dLa_L          dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult l3.log_PQ_LA                               if year == 2010 & rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  7,  4141) =   62.65
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0790
                                                       Root MSE      =  .07087

------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0157976   .0025385    -6.22   0.000    -.0207743   -.0108208
      dA_mze |   .0043187   .0010922     3.95   0.000     .0021774      .00646
             |
 rural_adult |
         L4. |  -.0772127   .0067807   -11.39   0.000    -.0905065   -.0639189
             |
  log_y_pc_r |
         L4. |   .0220176    .004453     4.94   0.000     .0132872     .030748
             |
log_pop_area |
         L4. |  -.0007286   .0010008    -0.73   0.467    -.0026908    .0012335
             |
 alpha_adult |
         L4. |  -.0044003   .0140409    -0.31   0.754    -.0319281    .0231275
             |
   log_PQ_LA |
         L3. |  -.0080429    .001567    -5.13   0.000    -.0111152   -.0049707
             |
       _cons |  -.0450053   .0157029    -2.87   0.004    -.0757915   -.0142192
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelB, ctitle(" ")                                          dec(3) nocons append
dir : seeout

. reg dLa_L          dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult              l4.log_ya_sct91               if year == 2010 & rf== 1, r

Linear regression                                      Number of obs =    3985
                                                       F(  7,  3977) =   46.41
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0709
                                                       Root MSE      =  .06957

------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   -.021002   .0023876    -8.80   0.000    -.0256831   -.0163209
      dA_mze |   .0055682   .0010657     5.22   0.000     .0034787    .0076576
             |
 rural_adult |
         L4. |  -.0754611   .0069428   -10.87   0.000    -.0890728   -.0618494
             |
  log_y_pc_r |
         L4. |   .0131787    .004653     2.83   0.005     .0040563    .0223011
             |
log_pop_area |
         L4. |  -.0001956   .0009653    -0.20   0.839    -.0020882    .0016971
             |
 alpha_adult |
         L4. |  -.0116941   .0143357    -0.82   0.415    -.0398001    .0164119
             |
log_ya_sct91 |
         L4. |  -.0028785   .0032006    -0.90   0.369    -.0091534    .0033965
             |
       _cons |  -.0430476   .0173204    -2.49   0.013    -.0770052   -.0090899
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelB, ctitle("Change in Employment share (2010-2000)")      dec(3) nocons append
dir : seeout

. reg dLa_L          dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult                            l4.La_L_sct91   if year == 2010 & rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  7,  4141) =  102.20
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1276
                                                       Root MSE      =  .06898

------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0113189   .0023901    -4.74   0.000    -.0160048   -.0066331
      dA_mze |   .0028633    .001037     2.76   0.006     .0008301    .0048964
             |
 rural_adult |
         L4. |   .0253862   .0099319     2.56   0.011     .0059143    .0448581
             |
  log_y_pc_r |
         L4. |   .0006067   .0044086     0.14   0.891    -.0080366      .00925
             |
log_pop_area |
         L4. |  -.0041772   .0010011    -4.17   0.000    -.0061398   -.0022145
             |
 alpha_adult |
         L4. |  -.0217461   .0137676    -1.58   0.114     -.048738    .0052459
             |
  La_L_sct91 |
         L4. |  -.1635686   .0111072   -14.73   0.000    -.1853447   -.1417926
             |
       _cons |   .0397312   .0168414     2.36   0.018      .006713    .0727495
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA4_PanelB, ctitle(" ")                                          dec(3) nocons append excel sortvar(dA_soy dA_mze l4.log_PQ_LA l3.log_
> ya_sct91 l3.La_L_sct91 l3.log_PQ_LA l4.log_ya_sct91 l4.La_L_sct91)
Tables\TableA4_PanelB.xml
dir : seeout

.         
. ****************************************************************************************************
. **** Table  A5: Effect of technological change on manufacturing                                 ****
. ****            Robustness to controlling for additional initial municipality characteristics   ****
. ****************************************************************************************************
. reg dLm_L   dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult l3.log_PQ_LA                             if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  7,  4141) =   58.23
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1018
                                                       Root MSE      =  .05395

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0129156   .0022195     5.82   0.000     .0085641    .0172671
      dA_mze |  -.0019077   .0009389    -2.03   0.042    -.0037484   -.0000669
             |
 rural_adult |
         L4. |    .012886   .0050188     2.57   0.010     .0030465    .0227255
             |
  log_y_pc_r |
         L4. |  -.0115568   .0032125    -3.60   0.000    -.0178551   -.0052585
             |
log_pop_area |
         L4. |   .0027754   .0006896     4.02   0.000     .0014233    .0041274
             |
 alpha_adult |
         L4. |   .0212946   .0099589     2.14   0.033     .0017697    .0408194
             |
   log_PQ_LA |
         L3. |   .0132486   .0012019    11.02   0.000     .0108922     .015605
             |
       _cons |   -.083397   .0108825    -7.66   0.000    -.1047325   -.0620615
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA5, ctitle(" ")                                      dec(3) nocons replace
dir : seeout

. reg dLm_L   dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult              l4.log_ya_sct91             if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    3985
                                                       F(  7,  3977) =   48.70
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0767
                                                       Root MSE      =  .05452

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0217997   .0020986    10.39   0.000     .0176853    .0259141
      dA_mze |  -.0043914   .0009135    -4.81   0.000    -.0061823   -.0026005
             |
 rural_adult |
         L4. |   .0115394   .0052571     2.20   0.028     .0012325    .0218463
             |
  log_y_pc_r |
         L4. |   .0080977   .0031862     2.54   0.011     .0018509    .0143445
             |
log_pop_area |
         L4. |   .0009856   .0006904     1.43   0.154     -.000368    .0023392
             |
 alpha_adult |
         L4. |   .0263108   .0102225     2.57   0.010      .006269    .0463527
             |
log_ya_sct91 |
         L4. |  -.0077715   .0020962    -3.71   0.000    -.0118812   -.0036618
             |
       _cons |  -.0366582   .0132492    -2.77   0.006    -.0626341   -.0106823
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA5, ctitle("Change in Employment share (2010-2000)") dec(3) nocons append
dir : seeout

. reg dLm_L   dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult                            l4.La_L_sct91 if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  7,  4141) =   62.45
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1179
                                                       Root MSE      =  .05346

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0144665   .0020403     7.09   0.000     .0104664    .0184666
      dA_mze |  -.0022193   .0008842    -2.51   0.012    -.0039527   -.0004859
             |
 rural_adult |
         L4. |  -.0600143   .0076543    -7.84   0.000    -.0750209   -.0450076
             |
  log_y_pc_r |
         L4. |   .0111238   .0032329     3.44   0.001     .0047855    .0174621
             |
log_pop_area |
         L4. |   .0045419   .0007087     6.41   0.000     .0031524    .0059314
             |
 alpha_adult |
         L4. |   .0408399   .0099211     4.12   0.000     .0213891    .0602906
             |
  La_L_sct91 |
         L4. |   .1142506   .0086391    13.22   0.000     .0973133    .1311879
             |
       _cons |  -.1259435   .0122841   -10.25   0.000     -.150027     -.10186
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA5, ctitle(" ")                                      dec(3) nocons append
dir : seeout

. reg dlog_Lm dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult l3.log_PQ_LA                             if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  7,  4141) =   55.40
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0904
                                                       Root MSE      =  .57999

------------------------------------------------------------------------------
             |               Robust
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1096208   .0211808     5.18   0.000      .068095    .1511466
      dA_mze |   -.021301   .0092027    -2.31   0.021    -.0393432   -.0032588
             |
 rural_adult |
         L4. |   .0706375   .0554466     1.27   0.203    -.0380677    .1793427
             |
  log_y_pc_r |
         L4. |   -.032376   .0376572    -0.86   0.390    -.1062042    .0414523
             |
log_pop_area |
         L4. |   .0303918   .0080634     3.77   0.000     .0145833    .0462004
             |
 alpha_adult |
         L4. |   .0767673   .1156872     0.66   0.507    -.1500417    .3035763
             |
   log_PQ_LA |
         L3. |   .1239544   .0132269     9.37   0.000     .0980225    .1498863
             |
       _cons |   -.902941   .1285016    -7.03   0.000    -1.154873   -.6510088
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA5, ctitle(" ")                                      dec(3) nocons append
dir : seeout

. reg dlog_Lm dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult              l4.log_ya_sct91             if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    3985
                                                       F(  7,  3977) =   47.39
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0700
                                                       Root MSE      =  .57502

------------------------------------------------------------------------------
             |               Robust
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1860258     .01963     9.48   0.000     .1475399    .2245118
      dA_mze |  -.0422921    .008981    -4.71   0.000       -.0599   -.0246842
             |
 rural_adult |
         L4. |   .0615794   .0579355     1.06   0.288    -.0520067    .1751656
             |
  log_y_pc_r |
         L4. |   .1316354   .0399824     3.29   0.001     .0532476    .2100233
             |
log_pop_area |
         L4. |   .0153531   .0079548     1.93   0.054    -.0002427    .0309489
             |
 alpha_adult |
         L4. |   .0958178   .1182918     0.81   0.418    -.1361004     .327736
             |
log_ya_sct91 |
         L4. |  -.0159085   .0227477    -0.70   0.484    -.0605067    .0286896
             |
       _cons |  -.6462974   .1392673    -4.64   0.000    -.9193393   -.3732555
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA5, ctitle("Change in Log employment (2010-2000)")   dec(3) nocons append
dir : seeout

. reg dlog_Lm dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult                            l4.La_L_sct91 if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  7,  4141) =   52.05
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0768
                                                       Root MSE      =   .5843

------------------------------------------------------------------------------
             |               Robust
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1548475   .0199908     7.75   0.000     .1156549    .1940402
      dA_mze |  -.0335368     .00897    -3.74   0.000    -.0511228   -.0159507
             |
 rural_adult |
         L4. |  -.2807798   .0795195    -3.53   0.000    -.4366807   -.1248789
             |
  log_y_pc_r |
         L4. |   .1365165   .0377958     3.61   0.000     .0624165    .2106165
             |
log_pop_area |
         L4. |   .0334538   .0083676     4.00   0.000     .0170488    .0498587
             |
 alpha_adult |
         L4. |   .2285763   .1175532     1.94   0.052     -.001891    .4590437
             |
  La_L_sct91 |
         L4. |   .5350282   .0873979     6.12   0.000     .3636813    .7063751
             |
       _cons |  -.9671667   .1385759    -6.98   0.000     -1.23885   -.6954836
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA5, ctitle(" ")                                      dec(3) nocons append
dir : seeout

. reg dlog_ym dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult l3.log_PQ_LA                             if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  7,  4141) =   29.04
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0449
                                                       Root MSE      =  .35685

------------------------------------------------------------------------------
             |               Robust
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0264658   .0133555    -1.98   0.048    -.0526497   -.0002819
      dA_mze |   .0146006   .0055717     2.62   0.009      .003677    .0255242
             |
 rural_adult |
         L4. |  -.0138374   .0347213    -0.40   0.690    -.0819097     .054235
             |
  log_y_pc_r |
         L4. |  -.1111732   .0272811    -4.08   0.000    -.1646587   -.0576877
             |
log_pop_area |
         L4. |  -.0350021   .0048522    -7.21   0.000    -.0445151   -.0254891
             |
 alpha_adult |
         L4. |   .0892064   .0752155     1.19   0.236    -.0582564    .2366692
             |
   log_PQ_LA |
         L3. |   .0038425   .0077126     0.50   0.618    -.0112782    .0189633
             |
       _cons |   .8171602   .0908348     9.00   0.000     .6390753    .9952451
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA5, ctitle(" ")                                      dec(3) nocons append
dir : seeout

. reg dlog_ym dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult              l4.log_ya_sct91             if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    3985
                                                       F(  7,  3977) =   29.15
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0452
                                                       Root MSE      =   .3506

------------------------------------------------------------------------------
             |               Robust
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0184074   .0125826    -1.46   0.144    -.0430763    .0062616
      dA_mze |   .0124896   .0054471     2.29   0.022     .0018103    .0231689
             |
 rural_adult |
         L4. |  -.0208185   .0360521    -0.58   0.564    -.0915009    .0498639
             |
  log_y_pc_r |
         L4. |  -.0919645   .0295411    -3.11   0.002    -.1498817   -.0340473
             |
log_pop_area |
         L4. |   -.035817   .0047862    -7.48   0.000    -.0452007   -.0264333
             |
 alpha_adult |
         L4. |   .0772216   .0783892     0.99   0.325    -.0764651    .2309083
             |
log_ya_sct91 |
         L4. |  -.0291865   .0157768    -1.85   0.064    -.0601179    .0017448
             |
       _cons |    .913407   .0948369     9.63   0.000     .7274736     1.09934
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA5, ctitle("Change in Log wage (2010-2000)")         dec(3) nocons append
dir : seeout

. reg dlog_ym dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult                            l4.La_L_sct91 if year == 2010 & rf == 1 , r

Linear regression                                      Number of obs =    4149
                                                       F(  7,  4141) =   32.94
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0471
                                                       Root MSE      =  .35644

------------------------------------------------------------------------------
             |               Robust
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0335337   .0127658    -2.63   0.009    -.0585616   -.0085058
      dA_mze |   .0167913   .0054436     3.08   0.002      .006119    .0274637
             |
 rural_adult |
         L4. |  -.1159027   .0492188    -2.35   0.019    -.2123979   -.0194074
             |
  log_y_pc_r |
         L4. |  -.0939955   .0261552    -3.59   0.000    -.1452739   -.0427172
             |
log_pop_area |
         L4. |  -.0311941   .0050845    -6.14   0.000    -.0411624   -.0212257
             |
 alpha_adult |
         L4. |   .1024763    .074825     1.37   0.171    -.0442209    .2491736
             |
  La_L_sct91 |
         L4. |   .1638059   .0535907     3.06   0.002     .0587393    .2688725
             |
       _cons |   .7231152   .0956958     7.56   0.000        .5355    .9107303
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA5, ctitle(" ")                                      dec(3) nocons append excel sortvar(dA_soy dA_mze l3.log_PQ_LA l4.log_ya_sct91 l4
> .La_L_sct91)
Tables\TableA5.xml
dir : seeout

.                 
. ****************************************************************************************************
. **** Table  A6: The effect of agricultural technological change on manufacturing and migration  ****
. ****            Robustness to controlling for pre-existing trends                               ****
. ****************************************************************************************************
. reg dlog_Lm        dA_soy_y2010 dA_mze_y2010 dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult y2010 if pre == 1, r

Linear regression                                      Number of obs =    7984
                                                       F(  9,  7974) =   34.37
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0314
                                                       Root MSE      =  .61224

------------------------------------------------------------------------------
             |               Robust
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dA_soy_y2010 |   .2344924   .0251144     9.34   0.000     .1852616    .2837231
dA_mze_y2010 |  -.0603357   .0121668    -4.96   0.000    -.0841858   -.0364855
      dA_soy |    .006778   .0200833     0.34   0.736    -.0325904    .0461465
      dA_mze |  -.0035355   .0095845    -0.37   0.712    -.0223235    .0152525
             |
 rural_adult |
         L4. |   .2550164   .0420614     6.06   0.000      .172565    .3374678
             |
  log_y_pc_r |
         L4. |   .0114336   .0269713     0.42   0.672    -.0414373    .0643045
             |
log_pop_area |
         L4. |  -.0157858   .0061052    -2.59   0.010    -.0277536    -.003818
             |
 alpha_adult |
         L4. |   .2449477   .0813465     3.01   0.003     .0854872    .4044081
             |
       y2010 |  -.2334304   .0321352    -7.26   0.000    -.2964237    -.170437
       _cons |  -.0698896   .0962528    -0.73   0.468    -.2585702     .118791
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA6, ctitle("Change in Log employment (2010-1991)") dec(3) nocons replace
dir : seeout

. reg dlog_ym        dA_soy_y2010 dA_mze_y2010 dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult y2010 if pre == 1, r

Linear regression                                      Number of obs =    7984
                                                       F(  9,  7974) =   15.36
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0180
                                                       Root MSE      =  .40207

------------------------------------------------------------------------------
             |               Robust
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dA_soy_y2010 |  -.1165755   .0176284    -6.61   0.000    -.1511318   -.0820191
dA_mze_y2010 |   .0528281   .0081854     6.45   0.000     .0367826    .0688737
      dA_soy |   .0623985   .0140236     4.45   0.000     .0349087    .0898884
      dA_mze |  -.0267694   .0064517    -4.15   0.000    -.0394164   -.0141223
             |
 rural_adult |
         L4. |   .0100556   .0271441     0.37   0.711     -.043154    .0632652
             |
  log_y_pc_r |
         L4. |  -.0578703   .0196623    -2.94   0.003    -.0964134   -.0193271
             |
log_pop_area |
         L4. |  -.0033108   .0039527    -0.84   0.402    -.0110592    .0044375
             |
 alpha_adult |
         L4. |   .1898955   .0524249     3.62   0.000     .0871289    .2926621
             |
       y2010 |   .1383922   .0221466     6.25   0.000      .094979    .1818053
       _cons |   .2942148   .0723659     4.07   0.000     .1523586    .4360709
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA6, ctitle("Change in Log wage (2010-1991)")       dec(3) nocons append
dir : seeout

. reg migration_rate dA_soy_y2010 dA_mze_y2010 dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult y2010 if pre == 1, r

Linear regression                                      Number of obs =    7984
                                                       F(  9,  7974) =  115.04
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0957
                                                       Root MSE      =  .14804

------------------------------------------------------------------------------
             |               Robust
migration_~e |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
dA_soy_y2010 |  -.0145255   .0060118    -2.42   0.016    -.0263101   -.0027408
dA_mze_y2010 |   .0100241   .0029274     3.42   0.001     .0042856    .0157626
      dA_soy |  -.0040874   .0052325    -0.78   0.435    -.0143445    .0061696
      dA_mze |  -.0028432   .0024767    -1.15   0.251    -.0076982    .0020118
             |
 rural_adult |
         L4. |  -.1282323   .0139704    -9.18   0.000    -.1556179   -.1008468
             |
  log_y_pc_r |
         L4. |   .0451076   .0064305     7.01   0.000     .0325021    .0577131
             |
log_pop_area |
         L4. |  -.0057045    .002234    -2.55   0.011    -.0100836   -.0013253
             |
 alpha_adult |
         L4. |   .0167132   .0185026     0.90   0.366    -.0195567    .0529832
             |
       y2010 |  -.0201504   .0064501    -3.12   0.002    -.0327943   -.0075064
       _cons |  -.1311781   .0267659    -4.90   0.000    -.1836464   -.0787099
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA6, ctitle("Migration rate (2010-1991)")            dec(3) nocons append excel
Tables\TableA6.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table  A8: The effect of agricultural technological change on manufacturing                ****
. ****            Robustness to excluding sectors directly linked to soy and maize                ****
. ****************************************************************************************************
. reg dLm0_L dA_soy dA_mze   l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010  & rf== 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   27.14
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0370
                                                       Root MSE      =   .0406

------------------------------------------------------------------------------
             |               Robust
      dLm0_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0127415   .0015625     8.15   0.000     .0096782    .0158048
      dA_mze |   -.004157   .0006559    -6.34   0.000    -.0054429   -.0028712
             |
 rural_adult |
         L4. |   .0122864   .0038172     3.22   0.001     .0048026    .0197703
             |
  log_y_pc_r |
         L4. |  -.0020592   .0022194    -0.93   0.354    -.0064104    .0022919
             |
log_pop_area |
         L4. |   .0027219    .000472     5.77   0.000     .0017965    .0036474
             |
 alpha_adult |
         L4. |   .0254388   .0071545     3.56   0.000     .0114121    .0394656
             |
       _cons |  -.0244573   .0081228    -3.01   0.003    -.0403823   -.0085322
------------------------------------------------------------------------------

.                 outreg2 using Tables\TableA8, ctitle("Change in Employment share (2010-2000)") dec(3) nocons  replace
dir : seeout

. reg dlog_Lm0   dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf== 1, r

Linear regression                                      Number of obs =    4134
                                                       F(  6,  4127) =   34.63
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0417
                                                       Root MSE      =  .61903

------------------------------------------------------------------------------
             |               Robust
    dlog_Lm0 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1671905   .0205269     8.14   0.000     .1269466    .2074344
      dA_mze |  -.0572154   .0093758    -6.10   0.000     -.075597   -.0388338
             |
 rural_adult |
         L4. |   .0417421   .0581188     0.72   0.473    -.0722022    .1556864
             |
  log_y_pc_r |
         L4. |    .074819   .0384281     1.95   0.052    -.0005208    .1501588
             |
log_pop_area |
         L4. |   .0343696   .0080275     4.28   0.000     .0186314    .0501078
             |
 alpha_adult |
         L4. |   .0864071    .124161     0.70   0.487    -.1570153    .3298295
             |
       _cons |  -.4293568    .133891    -3.21   0.001    -.6918554   -.1668582
------------------------------------------------------------------------------

.                 outreg2 using Tables\TableA8, ctitle("Change in Log employment (2010-2000)")   dec(3) nocons  append
dir : seeout

. reg dlog_ym0 dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010  & rf== 1, r

Linear regression                                      Number of obs =    4059
                                                       F(  6,  4052) =   24.73
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0300
                                                       Root MSE      =   .4495

------------------------------------------------------------------------------
             |               Robust
    dlog_ym0 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0105761   .0157479    -0.67   0.502    -.0414507    .0202984
      dA_mze |   .0102331   .0066508     1.54   0.124    -.0028062    .0232724
             |
 rural_adult |
         L4. |  -.0144239   .0440411    -0.33   0.743    -.1007686    .0719208
             |
  log_y_pc_r |
         L4. |  -.1170611   .0271516    -4.31   0.000    -.1702933    -.063829
             |
log_pop_area |
         L4. |   -.040131   .0061434    -6.53   0.000    -.0521755   -.0280866
             |
 alpha_adult |
         L4. |   .1438592   .0844683     1.70   0.089    -.0217451    .3094636
             |
       _cons |   .8582283    .100375     8.55   0.000     .6614382    1.055018
------------------------------------------------------------------------------

.                 outreg2 using Tables\TableA8, ctitle("Change in Log wage (2010-2000)")         dec(3) nocons  append excel
Tables\TableA8.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table A10: The effect of technological change on agriculture. Soy and maize expansion      ****
. ****            Robustness to correcting standard errors for spatial correlation                ****
. ****            a.   Robust standard errors                                                     ****
. ****************************************************************************************************
. reg dsoy_TA_w   dA_soy        l3.rural_adult                                                  if year ==2006 & fs == 1 & meso!=. & micro!=., r

Linear regression                                      Number of obs =    3652
                                                       F(  2,  3649) =   79.54
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0666
                                                       Root MSE      =  .04088

------------------------------------------------------------------------------
             |               Robust
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0133053   .0010822    12.29   0.000     .0111835     .015427
             |
 rural_adult |
         L3. |   .0198788   .0029061     6.84   0.000      .014181    .0255766
             |
       _cons |  -.0210946   .0022652    -9.31   0.000    -.0255358   -.0166533
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10a, ctitle("Change in Soy area share (2006-1996)")        dec(3)  nocons asterisk(se) replace
dir : seeout

. reg dsoy_TA_w   dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year ==2006 & fs == 1 & meso!=. & micro!=., r

Linear regression                                      Number of obs =    3652
                                                       F(  6,  3645) =   47.49
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1237
                                                       Root MSE      =  .03963

------------------------------------------------------------------------------
             |               Robust
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .012712   .0018971     6.70   0.000     .0089925    .0164315
      dA_mze |  -.0005212   .0007385    -0.71   0.480    -.0019691    .0009267
             |
 rural_adult |
         L3. |   .0394215   .0046957     8.40   0.000      .030215     .048628
             |
  log_y_pc_r |
         L3. |   .0008106   .0022051     0.37   0.713    -.0035127    .0051339
             |
log_pop_area |
         L3. |  -.0017568   .0004981    -3.53   0.000    -.0027334   -.0007802
             |
 alpha_adult |
         L3. |   .0643435   .0066634     9.66   0.000     .0512791    .0774079
             |
       _cons |   -.071237   .0097169    -7.33   0.000    -.0902881   -.0521859
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10a, ctitle(" ")                                   dec(3)  nocons asterisk(se) append
dir : seeout

. reg dmze_TA_w   dA_mze        l3.rural_adult                                                  if year ==2006 & fs == 1 & meso!=. & micro!=., r

Linear regression                                      Number of obs =    3652
                                                       F(  2,  3649) =   17.17
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0089
                                                       Root MSE      =  .06548

------------------------------------------------------------------------------
             |               Robust
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_mze |   .0032079   .0005907     5.43   0.000     .0020498     .004366
             |
 rural_adult |
         L3. |   .0112139   .0044196     2.54   0.011     .0025488    .0198791
             |
       _cons |  -.0068172   .0027641    -2.47   0.014    -.0122365   -.0013979
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10a, ctitle("Change in Maize area share (2006-1996)") dec(3)  nocons asterisk(se) append
dir : seeout

. reg dmze_TA_w   dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year ==2006 & fs == 1 & meso!=. & micro!=., r

Linear regression                                      Number of obs =    3652
                                                       F(  6,  3645) =    9.48
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0150
                                                       Root MSE      =  .06532

------------------------------------------------------------------------------
             |               Robust
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0008667   .0026734     0.32   0.746    -.0043749    .0061083
      dA_mze |   .0028532   .0010878     2.62   0.009     .0007205    .0049859
             |
 rural_adult |
         L3. |   .0100014   .0066597     1.50   0.133    -.0030557    .0230585
             |
  log_y_pc_r |
         L3. |  -.0046405   .0037919    -1.22   0.221    -.0120749    .0027938
             |
log_pop_area |
         L3. |   .0035167   .0006459     5.44   0.000     .0022504     .004783
             |
 alpha_adult |
         L3. |  -.0059855   .0119107    -0.50   0.615    -.0293378    .0173669
             |
       _cons |   .0070924   .0149021     0.48   0.634    -.0221249    .0363096
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10a, ctitle(" ")                                      dec(3)  nocons asterisk(se) append sortvar(dA_soy       dA_mze         l3.rural_adu
> lt l3.log_y_pc_r l3.log_pop_area l3.alpha_adult)  excel
Tables\TableA10a.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            b.   Standard errors clustered at micro-region level                            ****
. ****************************************************************************************************
. reg dsoy_TA_w   dA_soy        l3.rural_adult                                                  if year ==2006 & fs == 1 & meso!=. & micro!=., cluster(micro)

Linear regression                                      Number of obs =    3652
                                                       F(  2,   552) =   25.84
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0666
                                                       Root MSE      =  .04088

                                (Std. Err. adjusted for 553 clusters in micro)
------------------------------------------------------------------------------
             |               Robust
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0133053    .002046     6.50   0.000     .0092863    .0173242
             |
 rural_adult |
         L3. |   .0198788   .0043494     4.57   0.000     .0113354    .0284223
             |
       _cons |  -.0210946   .0035703    -5.91   0.000    -.0281077   -.0140815
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10b, ctitle("Change in Soy area share (2006-1996)")        dec(3)  nocons asterisk(se) replace
dir : seeout

. reg dsoy_TA_w   dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year ==2006 & fs == 1 & meso!=. & micro!=., cluster(micro)

Linear regression                                      Number of obs =    3652
                                                       F(  6,   552) =   16.62
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1237
                                                       Root MSE      =  .03963

                                (Std. Err. adjusted for 553 clusters in micro)
------------------------------------------------------------------------------
             |               Robust
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .012712   .0034733     3.66   0.000     .0058895    .0195345
      dA_mze |  -.0005212   .0013946    -0.37   0.709    -.0032605    .0022181
             |
 rural_adult |
         L3. |   .0394215   .0075646     5.21   0.000     .0245626    .0542804
             |
  log_y_pc_r |
         L3. |   .0008106   .0034447     0.24   0.814    -.0059558    .0075769
             |
log_pop_area |
         L3. |  -.0017568   .0007035    -2.50   0.013    -.0031386    -.000375
             |
 alpha_adult |
         L3. |   .0643435   .0095191     6.76   0.000     .0456453    .0830416
             |
       _cons |   -.071237   .0175181    -4.07   0.000    -.1056473   -.0368266
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10b, ctitle(" ")                                   dec(3)  nocons asterisk(se) append
dir : seeout

. reg dmze_TA_w   dA_mze        l3.rural_adult                                                  if year ==2006 & fs == 1 & meso!=. & micro!=., cluster(micro)

Linear regression                                      Number of obs =    3652
                                                       F(  2,   552) =    7.89
                                                       Prob > F      =  0.0004
                                                       R-squared     =  0.0089
                                                       Root MSE      =  .06548

                                (Std. Err. adjusted for 553 clusters in micro)
------------------------------------------------------------------------------
             |               Robust
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_mze |   .0032079   .0009628     3.33   0.001     .0013168    .0050991
             |
 rural_adult |
         L3. |   .0112139   .0056533     1.98   0.048     .0001092    .0223186
             |
       _cons |  -.0068172   .0036679    -1.86   0.064    -.0140219    .0003875
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10b, ctitle("Change in Maize area share (2006-1996)") dec(3)  nocons asterisk(se) append
dir : seeout

. reg dmze_TA_w   dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year ==2006 & fs == 1 & meso!=. & micro!=., cluster(micro)

Linear regression                                      Number of obs =    3652
                                                       F(  6,   552) =    4.35
                                                       Prob > F      =  0.0003
                                                       R-squared     =  0.0150
                                                       Root MSE      =  .06532

                                (Std. Err. adjusted for 553 clusters in micro)
------------------------------------------------------------------------------
             |               Robust
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0008667    .004245     0.20   0.838    -.0074716    .0092051
      dA_mze |   .0028532   .0016904     1.69   0.092    -.0004672    .0061736
             |
 rural_adult |
         L3. |   .0100014   .0081904     1.22   0.223    -.0060867    .0260895
             |
  log_y_pc_r |
         L3. |  -.0046405   .0044824    -1.04   0.301    -.0134452    .0041641
             |
log_pop_area |
         L3. |   .0035167   .0008873     3.96   0.000     .0017739    .0052595
             |
 alpha_adult |
         L3. |  -.0059855   .0142483    -0.42   0.675     -.033973    .0220021
             |
       _cons |   .0070924   .0194487     0.36   0.715    -.0311102    .0452949
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10b, ctitle(" ")                                      dec(3)  nocons asterisk(se) append sortvar(dA_soy       dA_mze         l3.rural_adu
> lt l3.log_y_pc_r l3.log_pop_area l3.alpha_adult)  excel
Tables\TableA10b.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            c.   Standard errors clustered at meso-region level                             ****
. ****************************************************************************************************
. reg dsoy_TA_w   dA_soy        l3.rural_adult                                                  if year ==2006 & fs == 1 & meso!=. & micro!=., cluster(meso)

Linear regression                                      Number of obs =    3652
                                                       F(  2,   113) =    7.30
                                                       Prob > F      =  0.0010
                                                       R-squared     =  0.0666
                                                       Root MSE      =  .04088

                                 (Std. Err. adjusted for 114 clusters in meso)
------------------------------------------------------------------------------
             |               Robust
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0133053   .0035308     3.77   0.000     .0063102    .0203003
             |
 rural_adult |
         L3. |   .0198788   .0092314     2.15   0.033     .0015898    .0381678
             |
       _cons |  -.0210946   .0068365    -3.09   0.003    -.0346389   -.0075502
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10c, ctitle("Change in Soy area share (2006-1996)")        dec(3)  nocons asterisk(se) replace
dir : seeout

. reg dsoy_TA_w   dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year ==2006 & fs == 1 & meso!=. & micro!=., cluster(meso)

Linear regression                                      Number of obs =    3652
                                                       F(  6,   113) =    5.47
                                                       Prob > F      =  0.0001
                                                       R-squared     =  0.1237
                                                       Root MSE      =  .03963

                                 (Std. Err. adjusted for 114 clusters in meso)
------------------------------------------------------------------------------
             |               Robust
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .012712    .005231     2.43   0.017     .0023485    .0230755
      dA_mze |  -.0005212   .0025474    -0.20   0.838    -.0055681    .0045258
             |
 rural_adult |
         L3. |   .0394215   .0134983     2.92   0.004     .0126788    .0661642
             |
  log_y_pc_r |
         L3. |   .0008106   .0054965     0.15   0.883     -.010079    .0117002
             |
log_pop_area |
         L3. |  -.0017568   .0008606    -2.04   0.044    -.0034618   -.0000518
             |
 alpha_adult |
         L3. |   .0643435   .0159662     4.03   0.000     .0327116    .0959753
             |
       _cons |   -.071237   .0342091    -2.08   0.040    -.1390114   -.0034625
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10c, ctitle(" ")                                   dec(3)  nocons asterisk(se) append
dir : seeout

. reg dmze_TA_w   dA_mze        l3.rural_adult                                                  if year ==2006 & fs == 1 & meso!=. & micro!=., cluster(meso)

Linear regression                                      Number of obs =    3652
                                                       F(  2,   113) =    4.42
                                                       Prob > F      =  0.0141
                                                       R-squared     =  0.0089
                                                       Root MSE      =  .06548

                                 (Std. Err. adjusted for 114 clusters in meso)
------------------------------------------------------------------------------
             |               Robust
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_mze |   .0032079   .0017176     1.87   0.064    -.0001949    .0066107
             |
 rural_adult |
         L3. |   .0112139   .0087667     1.28   0.203    -.0061546    .0285824
             |
       _cons |  -.0068172   .0046808    -1.46   0.148    -.0160907    .0024563
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10c, ctitle("Change in Maize area share (2006-1996)") dec(3)  nocons asterisk(se) append
dir : seeout

. reg dmze_TA_w   dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year ==2006 & fs == 1 & meso!=. & micro!=., cluster(meso)

Linear regression                                      Number of obs =    3652
                                                       F(  6,   113) =    2.85
                                                       Prob > F      =  0.0126
                                                       R-squared     =  0.0150
                                                       Root MSE      =  .06532

                                 (Std. Err. adjusted for 114 clusters in meso)
------------------------------------------------------------------------------
             |               Robust
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0008667   .0053143     0.16   0.871    -.0096619    .0113953
      dA_mze |   .0028532   .0021352     1.34   0.184    -.0013771    .0070834
             |
 rural_adult |
         L3. |   .0100014   .0116955     0.86   0.394    -.0131695    .0331723
             |
  log_y_pc_r |
         L3. |  -.0046405   .0052007    -0.89   0.374    -.0149442    .0056631
             |
log_pop_area |
         L3. |   .0035167   .0011592     3.03   0.003     .0012202    .0058132
             |
 alpha_adult |
         L3. |  -.0059855   .0150538    -0.40   0.692    -.0358098    .0238388
             |
       _cons |   .0070924   .0241234     0.29   0.769    -.0407005    .0548853
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10c, ctitle(" ")                                      dec(3)  nocons asterisk(se) append sortvar(dA_soy dA_mze l3.rural_adult l3.log_y_pc
> _r l3.log_pop_area l3.alpha_adult) excel
Tables\TableA10c.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            d.   Conley's standard errors: cutoff at 50 Km                                  ****
. ****************************************************************************************************
. capture program drop ols_spatial_HAC

. run ols_spatial_HAC.ado             /* This runs the program needed to compute spatial s.e. */

. 
. ols_spatial_HAC dsoy_TA_w   dA_soy        l3.rural_adult                                                      constant if year ==2006 & fs ==1 & meso!=. & micro!=., la
> t(latitude) lon(longitude) timevar(time) panelvar(AMC) dist( 50) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dsoy_TA_w
INDEPENDANT VARIABLES:  dA_soy L3.rural_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0133053   .0016345     8.14   0.000     .0101007    .0165098
             |
 rural_adult |
         L3. |   .0198788    .003988     4.98   0.000     .0120599    .0276977
             |
    constant |  -.0210946   .0031993    -6.59   0.000    -.0273673   -.0148219
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10d, ctitle("Change in Soy area share (2006-1996)")   dec(3)  drop(constant) nonotes label asterisk(se) replace
dir : seeout

. ols_spatial_HAC dsoy_TA_w   dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult constant if year ==2006 & fs ==1 & meso!=. & micro!=., lat(latitu
> de) lon(longitude) timevar(time) panelvar(AMC) dist( 50) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dsoy_TA_w
INDEPENDANT VARIABLES:  dA_soy dA_mze L3.rural_adult L3.log_y_pc_r L3.log_pop_area L3.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .012712   .0028639     4.44   0.000     .0070969    .0183271
      dA_mze |  -.0005212   .0011227    -0.46   0.643    -.0027224    .0016801
             |
 rural_adult |
         L3. |   .0394215   .0073771     5.34   0.000     .0249578    .0538852
             |
  log_y_pc_r |
         L3. |   .0008106   .0032414     0.25   0.803    -.0055445    .0071656
             |
log_pop_area |
         L3. |  -.0017568   .0005938    -2.96   0.003     -.002921   -.0005925
             |
 alpha_adult |
         L3. |   .0643435   .0085741     7.50   0.000     .0475329     .081154
             |
    constant |   -.071237   .0163982    -4.34   0.000    -.1033876   -.0390864
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10d, ctitle(" ")                                   dec(3)  drop(constant) nonotes label asterisk(se) append
dir : seeout

. ols_spatial_HAC dmze_TA_w          dA_mze l3.rural_adult                                              constant if year ==2006 & fs ==1 & meso!=. & micro!=., lat(latitu
> de) lon(longitude) timevar(time) panelvar(AMC) dist( 50) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dmze_TA_w
INDEPENDANT VARIABLES:  dA_mze L3.rural_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_mze |   .0032079   .0008397     3.82   0.000     .0015616    .0048542
             |
 rural_adult |
         L3. |   .0112139   .0053079     2.11   0.035     .0008071    .0216208
             |
    constant |  -.0068172   .0034271    -1.99   0.047    -.0135364   -.0000979
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10d, ctitle("Change in Maize area share (2006-1996)") dec(3)  drop(constant) nonotes label asterisk(se) append
dir : seeout

. ols_spatial_HAC dmze_TA_w   dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult constant if year ==2006 & fs ==1 & meso!=. & micro!=., lat(latitu
> de) lon(longitude) timevar(time) panelvar(AMC) dist( 50) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dmze_TA_w
INDEPENDANT VARIABLES:  dA_soy dA_mze L3.rural_adult L3.log_y_pc_r L3.log_pop_area L3.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0008667   .0039802     0.22   0.828    -.0069369    .0086703
      dA_mze |   .0028532   .0015591     1.83   0.067    -.0002036      .00591
             |
 rural_adult |
         L3. |   .0100014   .0078109     1.28   0.200    -.0053128    .0253156
             |
  log_y_pc_r |
         L3. |  -.0046405   .0044928    -1.03   0.302    -.0134491     .004168
             |
log_pop_area |
         L3. |   .0035167   .0007986     4.40   0.000     .0019509    .0050825
             |
 alpha_adult |
         L3. |  -.0059855   .0136163    -0.44   0.660    -.0326818    .0207109
             |
    constant |   .0070924   .0189124     0.38   0.708    -.0299875    .0441722
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10d, ctitle(" ")                                      dec(3)  drop(constant) nonotes label asterisk(se) append excel sortvar(dA_soy dA_mz
> e l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult) 
Tables\TableA10d.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            e.   Conley's standard errors: cutoff at 100 Km                                 ****
. ****************************************************************************************************
. ols_spatial_HAC dsoy_TA_w   dA_soy        l3.rural_adult                                                      constant if year ==2006 & fs ==1 & meso!=. & micro!=., la
> t(latitude) lon(longitude) timevar(time) panelvar(AMC) dist(100) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dsoy_TA_w
INDEPENDANT VARIABLES:  dA_soy L3.rural_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0133053   .0023821     5.59   0.000     .0086349    .0179756
             |
 rural_adult |
         L3. |   .0198788   .0052638     3.78   0.000     .0095586     .030199
             |
    constant |  -.0210946   .0043649    -4.83   0.000    -.0296525   -.0125367
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10e, ctitle("Change in Soy area share (2006-1996)")   dec(3)  drop(constant) nonotes label asterisk(se) replace
dir : seeout

. ols_spatial_HAC dsoy_TA_w   dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult constant if year ==2006 & fs ==1 & meso!=. & micro!=., lat(latitu
> de) lon(longitude) timevar(time) panelvar(AMC) dist(100) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dsoy_TA_w
INDEPENDANT VARIABLES:  dA_soy dA_mze L3.rural_adult L3.log_y_pc_r L3.log_pop_area L3.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .012712   .0040784     3.12   0.002     .0047159    .0207082
      dA_mze |  -.0005212   .0016577    -0.31   0.753    -.0037712    .0027288
             |
 rural_adult |
         L3. |   .0394215   .0094465     4.17   0.000     .0209006    .0579424
             |
  log_y_pc_r |
         L3. |   .0008106   .0041468     0.20   0.845    -.0073196    .0089408
             |
log_pop_area |
         L3. |  -.0017568   .0007032    -2.50   0.013    -.0031355   -.0003781
             |
 alpha_adult |
         L3. |   .0643435   .0115682     5.56   0.000     .0416626    .0870243
             |
    constant |   -.071237   .0219387    -3.25   0.001    -.1142503   -.0282237
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10e, ctitle(" ")                                   dec(3)  drop(constant) nonotes label asterisk(se) append
dir : seeout

. ols_spatial_HAC dmze_TA_w          dA_mze l3.rural_adult                                              constant if year ==2006 & fs ==1 & meso!=. & micro!=., lat(latitu
> de) lon(longitude) timevar(time) panelvar(AMC) dist(100) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dmze_TA_w
INDEPENDANT VARIABLES:  dA_mze L3.rural_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_mze |   .0032079   .0010989     2.92   0.004     .0010534    .0053624
             |
 rural_adult |
         L3. |   .0112139    .006541     1.71   0.087    -.0016105    .0240383
             |
    constant |  -.0068172   .0041624    -1.64   0.102     -.014978    .0013437
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10e, ctitle("Change in Maize area share (2006-1996)") dec(3)  drop(constant) nonotes label asterisk(se) append
dir : seeout

. ols_spatial_HAC dmze_TA_w   dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult constant if year ==2006 & fs ==1 & meso!=. & micro!=., lat(latitu
> de) lon(longitude) timevar(time) panelvar(AMC) dist(100) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dmze_TA_w
INDEPENDANT VARIABLES:  dA_soy dA_mze L3.rural_adult L3.log_y_pc_r L3.log_pop_area L3.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0008667   .0052148     0.17   0.868    -.0093575    .0110909
      dA_mze |   .0028532   .0020183     1.41   0.158    -.0011039    .0068103
             |
 rural_adult |
         L3. |   .0100014   .0091945     1.09   0.277    -.0080255    .0280282
             |
  log_y_pc_r |
         L3. |  -.0046405   .0051439    -0.90   0.367    -.0147257    .0054446
             |
log_pop_area |
         L3. |   .0035167   .0009399     3.74   0.000     .0016739    .0053595
             |
 alpha_adult |
         L3. |  -.0059855   .0164065    -0.36   0.715    -.0381524    .0261814
             |
    constant |   .0070924   .0220499     0.32   0.748    -.0361389    .0503237
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10e, ctitle(" ")                                      dec(3)  drop(constant) nonotes label asterisk(se) append excel sortvar(dA_soy dA_mz
> e l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult) 
Tables\TableA10e.xml
dir : seeout

.    
. ****************************************************************************************************
. ****            f.   Conley's standard errors: cutoff at 200 Km                                 ****
. ****************************************************************************************************
. ols_spatial_HAC dsoy_TA_w   dA_soy        l3.rural_adult                                                      constant if year ==2006 & fs ==1 & meso!=. & micro!=., la
> t(latitude) lon(longitude) timevar(time) panelvar(AMC) dist(200) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dsoy_TA_w
INDEPENDANT VARIABLES:  dA_soy L3.rural_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0133053   .0034929     3.81   0.000     .0064571    .0201534
             |
 rural_adult |
         L3. |   .0198788   .0076331     2.60   0.009     .0049133    .0348443
             |
    constant |  -.0210946   .0062024    -3.40   0.001     -.033255   -.0089341
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10f, ctitle("Change in Soy area share (2006-1996)")   dec(3)  drop(constant) nonotes label asterisk(se) replace
dir : seeout

. ols_spatial_HAC dsoy_TA_w   dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult constant if year ==2006 & fs ==1 & meso!=. & micro!=., lat(latitu
> de) lon(longitude) timevar(time) panelvar(AMC) dist(200) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dsoy_TA_w
INDEPENDANT VARIABLES:  dA_soy dA_mze L3.rural_adult L3.log_y_pc_r L3.log_pop_area L3.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .012712   .0057046     2.23   0.026     .0015275    .0238965
      dA_mze |  -.0005212   .0023973    -0.22   0.828    -.0052214    .0041791
             |
 rural_adult |
         L3. |   .0394215   .0123348     3.20   0.001     .0152377    .0636053
             |
  log_y_pc_r |
         L3. |   .0008106   .0055604     0.15   0.884    -.0100913    .0117124
             |
log_pop_area |
         L3. |  -.0017568   .0008973    -1.96   0.050    -.0035161    2.55e-06
             |
 alpha_adult |
         L3. |   .0643435   .0169735     3.79   0.000     .0310649     .097622
             |
    constant |   -.071237   .0298211    -2.39   0.017    -.1297047   -.0127692
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10f, ctitle(" ")                                   dec(3)  drop(constant) nonotes label asterisk(se) append
dir : seeout

. ols_spatial_HAC dmze_TA_w          dA_mze l3.rural_adult                                              constant if year ==2006 & fs ==1 & meso!=. & micro!=., lat(latitu
> de) lon(longitude) timevar(time) panelvar(AMC) dist(200) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dmze_TA_w
INDEPENDANT VARIABLES:  dA_mze L3.rural_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_mze |   .0032079   .0014467     2.22   0.027     .0003714    .0060444
             |
 rural_adult |
         L3. |   .0112139   .0082111     1.37   0.172     -.004885    .0273128
             |
    constant |  -.0068172   .0047204    -1.44   0.149     -.016072    .0024377
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10f, ctitle("Change in Maize area share (2006-1996)") dec(3)  drop(constant) nonotes label asterisk(se) append
dir : seeout

. ols_spatial_HAC dmze_TA_w   dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult constant if year ==2006 & fs ==1 & meso!=. & micro!=., lat(latitu
> de) lon(longitude) timevar(time) panelvar(AMC) dist(200) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dmze_TA_w
INDEPENDANT VARIABLES:  dA_soy dA_mze L3.rural_adult L3.log_y_pc_r L3.log_pop_area L3.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0008667   .0063595     0.14   0.892    -.0116019    .0133353
      dA_mze |   .0028532   .0024168     1.18   0.238    -.0018853    .0075917
             |
 rural_adult |
         L3. |   .0100014   .0092198     1.08   0.278     -.008075    .0280778
             |
  log_y_pc_r |
         L3. |  -.0046405    .005641    -0.82   0.411    -.0157004    .0064193
             |
log_pop_area |
         L3. |   .0035167   .0009844     3.57   0.000     .0015867    .0054468
             |
 alpha_adult |
         L3. |  -.0059855   .0173162    -0.35   0.730    -.0399358    .0279648
             |
    constant |   .0070924   .0227963     0.31   0.756    -.0376024    .0517871
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA10f, ctitle(" ")                                      dec(3)  drop(constant) nonotes label asterisk(se) append excel sortvar(dA_soy dA_mz
> e l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult) 
Tables\TableA10f.xml
dir : seeout

. 
.    
. ****************************************************************************************************
. **** Table A11: The effect of technological change on agriculture                               ****
. ****            Productivity, labor intensity and employment share                              ****
. ****            Robustness to correcting standard errors for spatial correlation                ****
. ****            a.   Robust standard errors                                                     ****
. ****************************************************************************************************
. reg dlog_PQ_LA     dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year == 2006 & rf == 1 & meso!=. & micro!=., r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =    7.85
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0121
                                                       Root MSE      =   .8062

------------------------------------------------------------------------------
             |               Robust
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1344694   .0272028     4.94   0.000     .0811373    .1878015
      dA_mze |  -.0331244   .0120088    -2.76   0.006     -.056668   -.0095808
             |
 rural_adult |
         L3. |   .1338137   .0751829     1.78   0.075    -.0135853    .2812126
             |
  log_y_pc_r |
         L3. |  -.0145714   .0477773    -0.30   0.760    -.1082406    .0790977
             |
log_pop_area |
         L3. |  -.0161204   .0123575    -1.30   0.192    -.0403477    .0081068
             |
 alpha_adult |
         L3. |  -.2898411     .14658    -1.98   0.048    -.5772166   -.0024657
             |
       _cons |   .6827405   .1776991     3.84   0.000     .3343548    1.031126
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA11a,     ctitle("Change in Log value per worker (2006-1996)") dec(3) nocons asterisk(se) replace     
dir : seeout

. reg dlog_LA_TA     dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year == 2006 & rf == 1 & meso!=. & micro!=., r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =    4.43
                                                       Prob > F      =  0.0002
                                                       R-squared     =  0.0069
                                                       Root MSE      =  .54929

------------------------------------------------------------------------------
             |               Robust
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0635392   .0205305    -3.09   0.002    -.1037901   -.0232884
      dA_mze |   .0329449   .0087442     3.77   0.000     .0158017    .0500882
             |
 rural_adult |
         L3. |  -.1767888   .0510959    -3.46   0.001    -.2769643   -.0766134
             |
  log_y_pc_r |
         L3. |   .0290896   .0388412     0.75   0.454    -.0470601    .1052393
             |
log_pop_area |
         L3. |  -.0169118    .010511    -1.61   0.108     -.037519    .0036954
             |
 alpha_adult |
         L3. |  -.1236139   .1158828    -1.07   0.286    -.3508065    .1035786
             |
       _cons |   .0794831   .1376234     0.58   0.564    -.1903327    .3492988
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA11a,     ctitle("Change in Log labor intensity (2006-1996)")  dec(3) nocons asterisk(se) append
dir : seeout

. reg dLa_L          dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1 & meso!=. & micro!=., r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   59.24
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0726
                                                       Root MSE      =  .07111

------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0207294    .002388    -8.68   0.000    -.0254113   -.0160476
      dA_mze |   .0057186   .0010607     5.39   0.000     .0036391    .0077982
             |
 rural_adult |
         L4. |  -.0759097   .0068275   -11.12   0.000    -.0892953    -.062524
             |
  log_y_pc_r |
         L4. |   .0138749   .0042515     3.26   0.001     .0055396    .0222101
             |
log_pop_area |
         L4. |  -.0000517   .0009788    -0.05   0.958    -.0019707    .0018674
             |
 alpha_adult |
         L4. |  -.0122312    .013984    -0.87   0.382    -.0396473     .015185
             |
       _cons |  -.0625452   .0152813    -4.09   0.000    -.0925047   -.0325858
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA11a,     ctitle("Change in Employment share (2010-2000)")     dec(3) nocons asterisk(se) append excel
Tables\TableA11a.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            b.   Standard errors clustered at micro-region level                            ****
. ****************************************************************************************************
. reg dlog_PQ_LA     dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year == 2006 & rf == 1 & meso!=. & micro!=., cluster(micro)

Linear regression                                      Number of obs =    4149
                                                       F(  6,   546) =    5.15
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0121
                                                       Root MSE      =   .8062

                                (Std. Err. adjusted for 547 clusters in micro)
------------------------------------------------------------------------------
             |               Robust
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1344694   .0331189     4.06   0.000     .0694133    .1995255
      dA_mze |  -.0331244    .015505    -2.14   0.033    -.0635811   -.0026678
             |
 rural_adult |
         L3. |   .1338137   .0796249     1.68   0.093     -.022595    .2902223
             |
  log_y_pc_r |
         L3. |  -.0145714   .0546757    -0.27   0.790    -.1219719     .092829
             |
log_pop_area |
         L3. |  -.0161204   .0154049    -1.05   0.296    -.0463805    .0141397
             |
 alpha_adult |
         L3. |  -.2898411    .173207    -1.67   0.095    -.6300748    .0503925
             |
       _cons |   .6827405   .1998325     3.42   0.001     .2902058    1.075275
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA11b,     ctitle("Change in Log value per worker (2006-1996)") dec(3) nocons asterisk(se) replace     
dir : seeout

. reg dlog_LA_TA     dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year == 2006 & rf == 1 & meso!=. & micro!=., cluster(micro)

Linear regression                                      Number of obs =    4149
                                                       F(  6,   546) =    2.64
                                                       Prob > F      =  0.0155
                                                       R-squared     =  0.0069
                                                       Root MSE      =  .54929

                                (Std. Err. adjusted for 547 clusters in micro)
------------------------------------------------------------------------------
             |               Robust
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0635392   .0264632    -2.40   0.017    -.1155214   -.0115571
      dA_mze |   .0329449   .0121192     2.72   0.007     .0091389     .056751
             |
 rural_adult |
         L3. |  -.1767888   .0565814    -3.12   0.002    -.2879326    -.065645
             |
  log_y_pc_r |
         L3. |   .0290896   .0421284     0.69   0.490    -.0536639    .1118432
             |
log_pop_area |
         L3. |  -.0169118   .0126477    -1.34   0.182    -.0417559    .0079324
             |
 alpha_adult |
         L3. |  -.1236139   .1339436    -0.92   0.356    -.3867217    .1394939
             |
       _cons |   .0794831   .1534327     0.52   0.605    -.2219075    .3808737
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA11b,     ctitle("Change in Log labor intensity (2006-1996)")  dec(3) nocons asterisk(se) append
dir : seeout

. reg dLa_L          dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1 & meso!=. & micro!=., cluster(micro)

Linear regression                                      Number of obs =    4149
                                                       F(  6,   546) =   31.25
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0726
                                                       Root MSE      =  .07111

                                (Std. Err. adjusted for 547 clusters in micro)
------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0207294   .0042443    -4.88   0.000    -.0290666   -.0123923
      dA_mze |   .0057186   .0018696     3.06   0.002     .0020461    .0093912
             |
 rural_adult |
         L4. |  -.0759097   .0092232    -8.23   0.000    -.0940269   -.0577925
             |
  log_y_pc_r |
         L4. |   .0138749   .0056481     2.46   0.014     .0027802    .0249695
             |
log_pop_area |
         L4. |  -.0000517   .0012925    -0.04   0.968    -.0025905    .0024871
             |
 alpha_adult |
         L4. |  -.0122312   .0187962    -0.65   0.515     -.049153    .0246907
             |
       _cons |  -.0625452    .021346    -2.93   0.004    -.1044755   -.0206149
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA11b,     ctitle("Change in Employment share (2010-2000)")     dec(3) nocons asterisk(se) append excel
Tables\TableA11b.xml
dir : seeout

.         
. ****************************************************************************************************
. ****            c.   Standard errors clustered at meso-region level                             ****
. ****************************************************************************************************
. reg dlog_PQ_LA     dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year == 2006 & rf == 1 & meso!=. & micro!=., cluster(meso)

Linear regression                                      Number of obs =    4149
                                                       F(  6,   111) =    4.70
                                                       Prob > F      =  0.0003
                                                       R-squared     =  0.0121
                                                       Root MSE      =   .8062

                                 (Std. Err. adjusted for 112 clusters in meso)
------------------------------------------------------------------------------
             |               Robust
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1344694   .0340266     3.95   0.000     .0670434    .2018954
      dA_mze |  -.0331244    .015986    -2.07   0.041    -.0648017   -.0014472
             |
 rural_adult |
         L3. |   .1338137   .0786946     1.70   0.092    -.0221249    .2897522
             |
  log_y_pc_r |
         L3. |  -.0145714   .0609385    -0.24   0.811    -.1353252    .1061823
             |
log_pop_area |
         L3. |  -.0161204   .0168473    -0.96   0.341    -.0495045    .0172636
             |
 alpha_adult |
         L3. |  -.2898411   .2068063    -1.40   0.164    -.6996417    .1199594
             |
       _cons |   .6827405   .2321538     2.94   0.004     .2227121    1.142769
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA11c,     ctitle("Change in Log value per worker (2006-1996)") dec(3) nocons asterisk(se) replace     
dir : seeout

. reg dlog_LA_TA     dA_soy dA_mze l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year == 2006 & rf == 1 & meso!=. & micro!=., cluster(meso)

Linear regression                                      Number of obs =    4149
                                                       F(  6,   111) =    1.81
                                                       Prob > F      =  0.1035
                                                       R-squared     =  0.0069
                                                       Root MSE      =  .54929

                                 (Std. Err. adjusted for 112 clusters in meso)
------------------------------------------------------------------------------
             |               Robust
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0635392    .029507    -2.15   0.033    -.1220094   -.0050691
      dA_mze |   .0329449   .0148227     2.22   0.028     .0035727    .0623171
             |
 rural_adult |
         L3. |  -.1767888   .0610641    -2.90   0.005    -.2977914   -.0557862
             |
  log_y_pc_r |
         L3. |   .0290896   .0493529     0.59   0.557    -.0687065    .1268858
             |
log_pop_area |
         L3. |  -.0169118   .0136151    -1.24   0.217    -.0438911    .0100675
             |
 alpha_adult |
         L3. |  -.1236139   .1435554    -0.86   0.391    -.4080784    .1608506
             |
       _cons |   .0794831   .1835832     0.43   0.666    -.2842992    .4432654
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA11c,     ctitle("Change in Log labor intensity (2006-1996)")  dec(3) nocons asterisk(se) append
dir : seeout

. reg dLa_L          dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1 & meso!=. & micro!=., cluster(meso)

Linear regression                                      Number of obs =    4149
                                                       F(  6,   111) =   20.18
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0726
                                                       Root MSE      =  .07111

                                 (Std. Err. adjusted for 112 clusters in meso)
------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0207294   .0062124    -3.34   0.001    -.0330396   -.0084193
      dA_mze |   .0057186   .0028917     1.98   0.050    -.0000116    .0114488
             |
 rural_adult |
         L4. |  -.0759097   .0110429    -6.87   0.000    -.0977919   -.0540275
             |
  log_y_pc_r |
         L4. |   .0138749   .0079777     1.74   0.085    -.0019335    .0296832
             |
log_pop_area |
         L4. |  -.0000517   .0017727    -0.03   0.977    -.0035644     .003461
             |
 alpha_adult |
         L4. |  -.0122312   .0253042    -0.48   0.630    -.0623732    .0379109
             |
       _cons |  -.0625452   .0304605    -2.05   0.042    -.1229047   -.0021858
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA11c,     ctitle("Change in Employment share (2010-2000)")     dec(3) nocons asterisk(se) append excel
Tables\TableA11c.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            d.   Conley's standard errors: cutoff at 50 Km                                  ****
. ****************************************************************************************************
. ols_spatial_HAC dlog_PQ_LA     dA_soy dA_mze l3.rural_adult     l3.log_y_pc_r l3.log_pop_area l3.alpha_adult constant if year == 2006 & rf ==1 & meso!=. & micro!=., la
> t(latitude) lon(longitude) timevar(time) panelvar(AMC) dist( 50) dropvar bartlett 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dlog_PQ_LA
INDEPENDANT VARIABLES:  dA_soy dA_mze L3.rural_adult L3.log_y_pc_r L3.log_pop_area L3.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1344694   .0324651     4.14   0.000     .0708203    .1981185
      dA_mze |  -.0331244   .0146091    -2.27   0.023    -.0617661   -.0044828
             |
 rural_adult |
         L3. |   .1338137   .0773343     1.73   0.084    -.0178031    .2854304
             |
  log_y_pc_r |
         L3. |  -.0145714   .0491489    -0.30   0.767    -.1109297    .0817868
             |
log_pop_area |
         L3. |  -.0161204   .0137448    -1.17   0.241    -.0430677    .0108268
             |
 alpha_adult |
         L3. |  -.2898411   .1625025    -1.78   0.075    -.6084333    .0287511
             |
    constant |   .6827405   .1805479     3.78   0.000     .3287696    1.036711
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA11d, ctitle("Change in Log value per worker (2006-1996)") dec(3)  drop(constant) nonotes label asterisk(se) replace
dir : seeout

. ols_spatial_HAC dlog_LA_TA     dA_soy dA_mze l3.rural_adult     l3.log_y_pc_r l3.log_pop_area l3.alpha_adult constant if year == 2006 & rf ==1 & meso!=. & micro!=., la
> t(latitude) lon(longitude) timevar(time) panelvar(AMC) dist( 50) dropvar bartlett 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dlog_LA_TA
INDEPENDANT VARIABLES:  dA_soy dA_mze L3.rural_adult L3.log_y_pc_r L3.log_pop_area L3.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0635392   .0241407    -2.63   0.009    -.1108679   -.0162106
      dA_mze |   .0329449   .0105533     3.12   0.002     .0122548     .053635
             |
 rural_adult |
         L3. |  -.1767888   .0521556    -3.39   0.001    -.2790417   -.0745359
             |
  log_y_pc_r |
         L3. |   .0290896   .0407285     0.71   0.475    -.0507601    .1089394
             |
log_pop_area |
         L3. |  -.0169118   .0104363    -1.62   0.105    -.0373725    .0035489
             |
 alpha_adult |
         L3. |  -.1236139   .1224468    -1.01   0.313    -.3636755    .1164476
             |
    constant |   .0794831   .1441305     0.55   0.581    -.2030901    .3620563
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA11d, ctitle("Change in Log labor intensity (2006-1996)")  dec(3)  drop(constant) nonotes label asterisk(se) append
dir : seeout

. ols_spatial_HAC dLa_L          dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult constant if year == 2010 & rf ==1 & meso!=. & micro!=., lat(la
> titude) lon(longitude) timevar(time) panelvar(AMC) dist( 50) dropvar bartlett 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dLa_L
INDEPENDANT VARIABLES:  dA_soy dA_mze L4.rural_adult L4.log_y_pc_r L4.log_pop_area L4.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0207294   .0034612    -5.99   0.000    -.0275153   -.0139436
      dA_mze |   .0057186   .0015142     3.78   0.000     .0027501    .0086872
             |
 rural_adult |
         L4. |  -.0759097   .0080788    -9.40   0.000    -.0917485   -.0600709
             |
  log_y_pc_r |
         L4. |   .0138749   .0050876     2.73   0.006     .0039004    .0238494
             |
log_pop_area |
         L4. |  -.0000517   .0011981    -0.04   0.966    -.0024006    .0022972
             |
 alpha_adult |
         L4. |  -.0122312   .0168011    -0.73   0.467    -.0451704    .0207081
             |
    constant |  -.0625452   .0188254    -3.32   0.001    -.0994532   -.0256372
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA11d, ctitle("Change in Employment share (2010-2000)")     dec(3)  drop(constant) nonotes label asterisk(se) append excel 
Tables\TableA11d.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            e.   Conley's standard errors: cutoff at 100 Km                                 ****
. ****************************************************************************************************
. ols_spatial_HAC dlog_PQ_LA     dA_soy dA_mze l3.rural_adult     l3.log_y_pc_r l3.log_pop_area l3.alpha_adult constant if year == 2006 & rf ==1 & meso!=. & micro!=., la
> t(latitude) lon(longitude) timevar(time) panelvar(AMC) dist(100) dropvar bartlett 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dlog_PQ_LA
INDEPENDANT VARIABLES:  dA_soy dA_mze L3.rural_adult L3.log_y_pc_r L3.log_pop_area L3.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1344694    .036904     3.64   0.000     .0621177    .2068211
      dA_mze |  -.0331244   .0163314    -2.03   0.043    -.0651426   -.0011062
             |
 rural_adult |
         L3. |   .1338137   .0807702     1.66   0.098    -.0245394    .2921667
             |
  log_y_pc_r |
         L3. |  -.0145714   .0525113    -0.28   0.781    -.1175219     .088379
             |
log_pop_area |
         L3. |  -.0161204   .0140517    -1.15   0.251    -.0436692    .0114283
             |
 alpha_adult |
         L3. |  -.2898411   .1746824    -1.66   0.097    -.6323123    .0526301
             |
    constant |   .6827405    .189316     3.61   0.000     .3115795    1.053901
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA11e, ctitle("Change in Log value per worker (2006-1996)") dec(3)  drop(constant) nonotes label asterisk(se) replace
dir : seeout

. ols_spatial_HAC dlog_LA_TA     dA_soy dA_mze l3.rural_adult     l3.log_y_pc_r l3.log_pop_area l3.alpha_adult constant if year == 2006 & rf ==1 & meso!=. & micro!=., la
> t(latitude) lon(longitude) timevar(time) panelvar(AMC) dist(100) dropvar bartlett 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dlog_LA_TA
INDEPENDANT VARIABLES:  dA_soy dA_mze L3.rural_adult L3.log_y_pc_r L3.log_pop_area L3.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0635392   .0284438    -2.23   0.026    -.1193044    -.007774
      dA_mze |   .0329449    .013122     2.51   0.012     .0072188    .0586711
             |
 rural_adult |
         L3. |  -.1767888   .0562073    -3.15   0.002    -.2869853   -.0665923
             |
  log_y_pc_r |
         L3. |   .0290896   .0440147     0.66   0.509    -.0572028    .1153821
             |
log_pop_area |
         L3. |  -.0169118   .0107429    -1.57   0.116    -.0379737    .0041501
             |
 alpha_adult |
         L3. |  -.1236139   .1288576    -0.96   0.337    -.3762439    .1290161
             |
    constant |   .0794831   .1571592     0.51   0.613    -.2286334    .3875995
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA11e, ctitle("Change in Log labor intensity (2006-1996)")  dec(3)  drop(constant) nonotes label asterisk(se) append
dir : seeout

. ols_spatial_HAC dLa_L          dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult constant if year == 2010 & rf ==1 & meso!=. & micro!=., lat(la
> titude) lon(longitude) timevar(time) panelvar(AMC) dist(100) dropvar bartlett 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dLa_L
INDEPENDANT VARIABLES:  dA_soy dA_mze L4.rural_adult L4.log_y_pc_r L4.log_pop_area L4.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0207294   .0047905    -4.33   0.000    -.0301215   -.0113374
      dA_mze |   .0057186   .0021074     2.71   0.007     .0015869    .0098503
             |
 rural_adult |
         L4. |  -.0759097   .0100351    -7.56   0.000    -.0955838   -.0562355
             |
  log_y_pc_r |
         L4. |   .0138749   .0061334     2.26   0.024       .00185    .0258997
             |
log_pop_area |
         L4. |  -.0000517   .0014632    -0.04   0.972    -.0029204     .002817
             |
 alpha_adult |
         L4. |  -.0122312   .0205791    -0.59   0.552    -.0525772    .0281149
             |
    constant |  -.0625452   .0235452    -2.66   0.008    -.1087065   -.0163839
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA11e, ctitle("Change in Employment share (2010-2000)")     dec(3)  drop(constant) nonotes label asterisk(se) append excel 
Tables\TableA11e.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            f.   Conley's standard errors: cutoff at 200 Km                                 ****
. ****************************************************************************************************
. ols_spatial_HAC dlog_PQ_LA     dA_soy dA_mze l3.rural_adult     l3.log_y_pc_r l3.log_pop_area l3.alpha_adult constant if year == 2006 & rf ==1 & meso!=. & micro!=., la
> t(latitude) lon(longitude) timevar(time) panelvar(AMC) dist(200) dropvar bartlett 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dlog_PQ_LA
INDEPENDANT VARIABLES:  dA_soy dA_mze L3.rural_adult L3.log_y_pc_r L3.log_pop_area L3.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1344694   .0389517     3.45   0.001     .0581031    .2108357
      dA_mze |  -.0331244   .0171742    -1.93   0.054    -.0667951    .0005462
             |
 rural_adult |
         L3. |   .1338137   .0809078     1.65   0.098    -.0248091    .2924364
             |
  log_y_pc_r |
         L3. |  -.0145714   .0557811    -0.26   0.794    -.1239324    .0947895
             |
log_pop_area |
         L3. |  -.0161204   .0158725    -1.02   0.310     -.047239    .0149981
             |
 alpha_adult |
         L3. |  -.2898411   .1893947    -1.53   0.126    -.6611564    .0814742
             |
    constant |   .6827405   .1977044     3.45   0.001     .2951338    1.070347
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA11f, ctitle("Change in Log value per worker (2006-1996)") dec(3)  drop(constant) nonotes label asterisk(se) replace
dir : seeout

. ols_spatial_HAC dlog_LA_TA     dA_soy dA_mze l3.rural_adult     l3.log_y_pc_r l3.log_pop_area l3.alpha_adult constant if year == 2006 & rf ==1 & meso!=. & micro!=., la
> t(latitude) lon(longitude) timevar(time) panelvar(AMC) dist(200) dropvar bartlett 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dlog_LA_TA
INDEPENDANT VARIABLES:  dA_soy dA_mze L3.rural_adult L3.log_y_pc_r L3.log_pop_area L3.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0635392   .0320247    -1.98   0.047    -.1263248   -.0007536
      dA_mze |   .0329449    .015396     2.14   0.032     .0027606    .0631293
             |
 rural_adult |
         L3. |  -.1767888   .0648466    -2.73   0.006    -.3039229   -.0496548
             |
  log_y_pc_r |
         L3. |   .0290896   .0435542     0.67   0.504    -.0562999    .1144792
             |
log_pop_area |
         L3. |  -.0169118   .0114321    -1.48   0.139    -.0393248    .0055012
             |
 alpha_adult |
         L3. |  -.1236139   .1306356    -0.95   0.344    -.3797299     .132502
             |
    constant |   .0794831   .1589656     0.50   0.617    -.2321748    .3911409
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA11f, ctitle("Change in Log labor intensity (2006-1996)")  dec(3)  drop(constant) nonotes label asterisk(se) append
dir : seeout

. ols_spatial_HAC dLa_L          dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult constant if year == 2010 & rf ==1 & meso!=. & micro!=., lat(la
> titude) lon(longitude) timevar(time) panelvar(AMC) dist(200) dropvar bartlett 
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dLa_L
INDEPENDANT VARIABLES:  dA_soy dA_mze L4.rural_adult L4.log_y_pc_r L4.log_pop_area L4.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0207294    .006205    -3.34   0.001    -.0328945   -.0085644
      dA_mze |   .0057186   .0027007     2.12   0.034     .0004239    .0110134
             |
 rural_adult |
         L4. |  -.0759097   .0127078    -5.97   0.000    -.1008238   -.0509955
             |
  log_y_pc_r |
         L4. |   .0138749   .0074031     1.87   0.061    -.0006392    .0283889
             |
log_pop_area |
         L4. |  -.0000517   .0017986    -0.03   0.977    -.0035779    .0034745
             |
 alpha_adult |
         L4. |  -.0122312   .0254326    -0.48   0.631    -.0620927    .0376304
             |
    constant |  -.0625452    .028978    -2.16   0.031    -.1193576   -.0057328
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA11f, ctitle("Change in Employment share (2010-2000)")     dec(3)  drop(constant) nonotes label asterisk(se) append excel 
Tables\TableA11f.xml
dir : seeout

.         
. ****************************************************************************************************
. **** Table A12: The effect of agricultural technological change on manufacturing                ****
. ****            Employment share, employment and wages                                          ****
. ****            Robustness to correcting standard errors for spatial correlation                ****
. ****            a.   Robust standard errors                                                     ****
. ****************************************************************************************************
. reg dLm_L   dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   53.34
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0728
                                                       Root MSE      =  .05481

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0210396   .0020852    10.09   0.000     .0169514    .0251277
      dA_mze |  -.0042137   .0009034    -4.66   0.000    -.0059849   -.0024425
             |
 rural_adult |
         L4. |   .0107396   .0051103     2.10   0.036     .0007206    .0207586
             |
  log_y_pc_r |
         L4. |   .0018562   .0031071     0.60   0.550    -.0042354    .0079478
             |
log_pop_area |
         L4. |   .0016603    .000708     2.35   0.019     .0002723    .0030484
             |
 alpha_adult |
         L4. |   .0341938   .0100351     3.41   0.001     .0145195    .0538681
             |
       _cons |  -.0545047   .0107549    -5.07   0.000    -.0755902   -.0334192
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA12a,     ctitle("Change in Employment share (2010-2000)") dec(3)  nocons asterisk(se) replace
dir : seeout

. reg dlog_Lm dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   55.62
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0681
                                                       Root MSE      =  .58697

------------------------------------------------------------------------------
             |               Robust
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .185629   .0195433     9.50   0.000     .1473136    .2239444
      dA_mze |  -.0428767   .0089266    -4.80   0.000    -.0603776   -.0253757
             |
 rural_adult |
         L4. |   .0505559   .0563602     0.90   0.370    -.0599403    .1610521
             |
  log_y_pc_r |
         L4. |   .0931167   .0368444     2.53   0.012     .0208819    .1653515
             |
log_pop_area |
         L4. |   .0199595   .0081217     2.46   0.014     .0040366    .0358824
             |
 alpha_adult |
         L4. |   .1974533   .1174613     1.68   0.093     -.032834    .4277406
             |
       _cons |  -.6326233    .126915    -4.98   0.000     -.881445   -.3838017
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA12a,     ctitle("Change in Log employment (2010-2000)")   dec(3)  nocons asterisk(se) append
dir : seeout

. reg dlog_ym dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   33.12
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0448
                                                       Root MSE      =  .35682

------------------------------------------------------------------------------
             |               Robust
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0241096   .0124821    -1.93   0.053    -.0485812     .000362
      dA_mze |   .0139318   .0054323     2.56   0.010     .0032817    .0245819
             |
 rural_adult |
         L4. |  -.0144599   .0348133    -0.42   0.678    -.0827127    .0537929
             |
  log_y_pc_r |
         L4. |   -.107283   .0258166    -4.16   0.000    -.1578974   -.0566686
             |
log_pop_area |
         L4. |  -.0353255   .0047947    -7.37   0.000    -.0447257   -.0259253
             |
 alpha_adult |
         L4. |   .0929476   .0751947     1.24   0.216    -.0544744    .2403696
             |
       _cons |     .82554   .0896006     9.21   0.000     .6498747    1.001205
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA12a,     ctitle("Change in Log wage (2010-2000)")         dec(3)  nocons asterisk(se) append excel
Tables\TableA12a.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            b.   Standard errors clustered at micro-region level                            ****
. ****************************************************************************************************
. reg dLm_L   dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, cluster(micro)

Linear regression                                      Number of obs =    4149
                                                       F(  6,   546) =   15.85
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0728
                                                       Root MSE      =  .05481

                                (Std. Err. adjusted for 547 clusters in micro)
------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0210396   .0038603     5.45   0.000     .0134568    .0286224
      dA_mze |  -.0042137   .0017581    -2.40   0.017    -.0076672   -.0007603
             |
 rural_adult |
         L4. |   .0107396   .0075326     1.43   0.155    -.0040568    .0255361
             |
  log_y_pc_r |
         L4. |   .0018562   .0043713     0.42   0.671    -.0067305    .0104428
             |
log_pop_area |
         L4. |   .0016603   .0011281     1.47   0.142    -.0005555    .0038762
             |
 alpha_adult |
         L4. |   .0341938   .0145706     2.35   0.019     .0055726     .062815
             |
       _cons |  -.0545047   .0157821    -3.45   0.001    -.0855057   -.0235037
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA12b,     ctitle("Change in Employment share (2010-2000)") dec(3)  nocons asterisk(se) replace
dir : seeout

. reg dlog_Lm dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, cluster(micro)

Linear regression                                      Number of obs =    4149
                                                       F(  6,   546) =   27.72
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0681
                                                       Root MSE      =  .58697

                                (Std. Err. adjusted for 547 clusters in micro)
------------------------------------------------------------------------------
             |               Robust
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .185629   .0296025     6.27   0.000     .1274802    .2437778
      dA_mze |  -.0428767    .014264    -3.01   0.003    -.0708957   -.0148576
             |
 rural_adult |
         L4. |   .0505559   .0705576     0.72   0.474    -.0880417    .1891535
             |
  log_y_pc_r |
         L4. |   .0931167    .045135     2.06   0.040     .0044571    .1817763
             |
log_pop_area |
         L4. |   .0199595   .0104811     1.90   0.057    -.0006288    .0405478
             |
 alpha_adult |
         L4. |   .1974533   .1515497     1.30   0.193    -.1002385    .4951452
             |
       _cons |  -.6326233    .160041    -3.95   0.000    -.9469947    -.318252
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA12b,     ctitle("Change in Log employment (2010-2000)")   dec(3)  nocons asterisk(se) append
dir : seeout

. reg dlog_ym dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, cluster(micro)

Linear regression                                      Number of obs =    4149
                                                       F(  6,   546) =   29.19
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0448
                                                       Root MSE      =  .35682

                                (Std. Err. adjusted for 547 clusters in micro)
------------------------------------------------------------------------------
             |               Robust
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0241096   .0139222    -1.73   0.084    -.0514572     .003238
      dA_mze |   .0139318   .0061244     2.27   0.023     .0019014    .0259622
             |
 rural_adult |
         L4. |  -.0144599    .037419    -0.39   0.699    -.0879628     .059043
             |
  log_y_pc_r |
         L4. |   -.107283   .0254362    -4.22   0.000    -.1572477   -.0573182
             |
log_pop_area |
         L4. |  -.0353255   .0052214    -6.77   0.000     -.045582   -.0250691
             |
 alpha_adult |
         L4. |   .0929476   .0745004     1.25   0.213    -.0533948      .23929
             |
       _cons |     .82554   .0927309     8.90   0.000      .643387    1.007693
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA12b,     ctitle("Change in Log wage (2010-2000)")         dec(3)  nocons asterisk(se) append excel
Tables\TableA12b.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            c.   Standard errors clustered at meso-region level                             ****
. ****************************************************************************************************
. reg dLm_L   dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, cluster(meso)

Linear regression                                      Number of obs =    4149
                                                       F(  6,   111) =    7.15
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0728
                                                       Root MSE      =  .05481

                                 (Std. Err. adjusted for 112 clusters in meso)
------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0210396   .0060284     3.49   0.001     .0090938    .0329854
      dA_mze |  -.0042137   .0033531    -1.26   0.212    -.0108582    .0024307
             |
 rural_adult |
         L4. |   .0107396   .0094398     1.14   0.258     -.007966    .0294452
             |
  log_y_pc_r |
         L4. |   .0018562   .0063532     0.29   0.771     -.010733    .0144454
             |
log_pop_area |
         L4. |   .0016603   .0014965     1.11   0.270     -.001305    .0046257
             |
 alpha_adult |
         L4. |   .0341938   .0201829     1.69   0.093    -.0057999    .0741875
             |
       _cons |  -.0545047   .0251425    -2.17   0.032    -.1043262   -.0046832
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA12c,     ctitle("Change in Employment share (2010-2000)") dec(3)  nocons asterisk(se) replace
dir : seeout

. reg dlog_Lm dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, cluster(meso)

Linear regression                                      Number of obs =    4149
                                                       F(  6,   111) =   13.31
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0681
                                                       Root MSE      =  .58697

                                 (Std. Err. adjusted for 112 clusters in meso)
------------------------------------------------------------------------------
             |               Robust
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .185629   .0447428     4.15   0.000     .0969682    .2742898
      dA_mze |  -.0428767   .0268542    -1.60   0.113    -.0960901    .0103368
             |
 rural_adult |
         L4. |   .0505559   .0745678     0.68   0.499    -.0972052     .198317
             |
  log_y_pc_r |
         L4. |   .0931167   .0598133     1.56   0.122    -.0254074    .2116408
             |
log_pop_area |
         L4. |   .0199595   .0126888     1.57   0.119    -.0051842    .0451032
             |
 alpha_adult |
         L4. |   .1974533   .1787411     1.10   0.272    -.1567341    .5516407
             |
       _cons |  -.6326233   .2338842    -2.70   0.008    -1.096081   -.1691662
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA12c,     ctitle("Change in Log employment (2010-2000)")   dec(3)  nocons asterisk(se) append
dir : seeout

. reg dlog_ym dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, cluster(meso)

Linear regression                                      Number of obs =    4149
                                                       F(  6,   111) =   26.24
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0448
                                                       Root MSE      =  .35682

                                 (Std. Err. adjusted for 112 clusters in meso)
------------------------------------------------------------------------------
             |               Robust
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0241096   .0193183    -1.25   0.215    -.0623901    .0141709
      dA_mze |   .0139318   .0080574     1.73   0.087    -.0020344     .029898
             |
 rural_adult |
         L4. |  -.0144599   .0591903    -0.24   0.807    -.1317494    .1028297
             |
  log_y_pc_r |
         L4. |   -.107283   .0263751    -4.07   0.000     -.159547    -.055019
             |
log_pop_area |
         L4. |  -.0353255   .0071698    -4.93   0.000     -.049533    -.021118
             |
 alpha_adult |
         L4. |   .0929476   .0879872     1.06   0.293    -.0814048    .2673001
             |
       _cons |     .82554   .1125742     7.33   0.000     .6024668    1.048613
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA12c,     ctitle("Change in Log wage (2010-2000)")         dec(3)  nocons asterisk(se) append excel
Tables\TableA12c.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            d.   Conley's standard errors: cutoff at 50 Km                                  ****
. ****************************************************************************************************
. ols_spatial_HAC dLm_L   dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult constant if year == 2010 & rf ==1, lat(latitude) lon(longitude) timev
> ar(time) panelvar(AMC) dist( 50) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dLm_L
INDEPENDANT VARIABLES:  dA_soy dA_mze L4.rural_adult L4.log_y_pc_r L4.log_pop_area L4.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0210396   .0031003     6.79   0.000     .0149614    .0271178
      dA_mze |  -.0042137   .0013953    -3.02   0.003    -.0069492   -.0014782
             |
 rural_adult |
         L4. |   .0107396   .0062641     1.71   0.087    -.0015414    .0230206
             |
  log_y_pc_r |
         L4. |   .0018562   .0037982     0.49   0.625    -.0055902    .0093026
             |
log_pop_area |
         L4. |   .0016603   .0009985     1.66   0.096    -.0002972    .0036178
             |
 alpha_adult |
         L4. |   .0341938   .0128188     2.67   0.008      .009062    .0593256
             |
    constant |  -.0545047   .0137048    -3.98   0.000    -.0813735   -.0276359
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA12d, ctitle("Change in Employment share (2010-2000)") dec(3)  drop(constant) nonotes label asterisk(se) replace
dir : seeout

. ols_spatial_HAC dlog_Lm dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult constant if year == 2010 & rf ==1, lat(latitude) lon(longitude) timev
> ar(time) panelvar(AMC) dist( 50) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dlog_Lm
INDEPENDANT VARIABLES:  dA_soy dA_mze L4.rural_adult L4.log_y_pc_r L4.log_pop_area L4.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .185629     .02435     7.62   0.000       .13789     .233368
      dA_mze |  -.0428767   .0114222    -3.75   0.000    -.0652704    -.020483
             |
 rural_adult |
         L4. |   .0505559   .0633439     0.80   0.425    -.0736322    .1747441
             |
  log_y_pc_r |
         L4. |   .0931167   .0403109     2.31   0.021     .0140856    .1721477
             |
log_pop_area |
         L4. |   .0199595   .0095388     2.09   0.036     .0012583    .0386607
             |
 alpha_adult |
         L4. |   .1974533   .1376939     1.43   0.152    -.0725007    .4674074
             |
    constant |  -.6326233   .1402813    -4.51   0.000    -.9076501   -.3575966
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA12d, ctitle("Change in Log employment (2010-2000)")   dec(3)  drop(constant) nonotes label asterisk(se) append
dir : seeout

. ols_spatial_HAC dlog_ym dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult constant if year == 2010 & rf ==1, lat(latitude) lon(longitude) timev
> ar(time) panelvar(AMC) dist( 50) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dlog_ym
INDEPENDANT VARIABLES:  dA_soy dA_mze L4.rural_adult L4.log_y_pc_r L4.log_pop_area L4.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 50 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0241096   .0135859    -1.77   0.076    -.0507452     .002526
      dA_mze |   .0139318    .005849     2.38   0.017     .0024647    .0253989
             |
 rural_adult |
         L4. |  -.0144599   .0366915    -0.39   0.694    -.0863949    .0574752
             |
  log_y_pc_r |
         L4. |   -.107283   .0259755    -4.13   0.000     -.158209    -.056357
             |
log_pop_area |
         L4. |  -.0353255   .0049963    -7.07   0.000    -.0451208   -.0255302
             |
 alpha_adult |
         L4. |   .0929476    .077036     1.21   0.228    -.0580842    .2439795
             |
    constant |     .82554   .0912534     9.05   0.000     .6466344    1.004446
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA12d, ctitle("Change in Log wage (2010-2000)")         dec(3)  drop(constant) nonotes label asterisk(se) append excel
Tables\TableA12d.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            e.   Conley's standard errors: cutoff at 100 Km                                 ****
. ****************************************************************************************************
. ols_spatial_HAC dLm_L   dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult constant if year == 2010 & rf ==1, lat(latitude) lon(longitude) timev
> ar(time) panelvar(AMC) dist(100) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dLm_L
INDEPENDANT VARIABLES:  dA_soy dA_mze L4.rural_adult L4.log_y_pc_r L4.log_pop_area L4.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0210396   .0042995     4.89   0.000     .0126103    .0294689
      dA_mze |  -.0042137   .0020095    -2.10   0.036    -.0081535    -.000274
             |
 rural_adult |
         L4. |   .0107396   .0076696     1.40   0.162    -.0042969    .0257761
             |
  log_y_pc_r |
         L4. |   .0018562   .0047215     0.39   0.694    -.0074005    .0111129
             |
log_pop_area |
         L4. |   .0016603   .0012773     1.30   0.194    -.0008439    .0041645
             |
 alpha_adult |
         L4. |   .0341938   .0162724     2.10   0.036     .0022912    .0660964
             |
    constant |  -.0545047   .0172013    -3.17   0.002    -.0882285   -.0207809
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA12e, ctitle("Change in Employment share (2010-2000)") dec(3)  drop(constant) nonotes label asterisk(se) replace
dir : seeout

. ols_spatial_HAC dlog_Lm dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult constant if year == 2010 & rf ==1, lat(latitude) lon(longitude) timev
> ar(time) panelvar(AMC) dist(100) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dlog_Lm
INDEPENDANT VARIABLES:  dA_soy dA_mze L4.rural_adult L4.log_y_pc_r L4.log_pop_area L4.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .185629   .0303195     6.12   0.000     .1261865    .2450715
      dA_mze |  -.0428767   .0147708    -2.90   0.004    -.0718354   -.0139179
             |
 rural_adult |
         L4. |   .0505559   .0722624     0.70   0.484    -.0911172     .192229
             |
  log_y_pc_r |
         L4. |   .0931167   .0464898     2.00   0.045     .0019718    .1842616
             |
log_pop_area |
         L4. |   .0199595   .0110996     1.80   0.072    -.0018016    .0417206
             |
 alpha_adult |
         L4. |   .1974533   .1667896     1.18   0.237    -.1295438    .5244504
             |
    constant |  -.6326233   .1624001    -3.90   0.000    -.9510147    -.314232
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA12e, ctitle("Change in Log employment (2010-2000)")   dec(3)  drop(constant) nonotes label asterisk(se) append
dir : seeout

. ols_spatial_HAC dlog_ym dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult constant if year == 2010 & rf ==1, lat(latitude) lon(longitude) timev
> ar(time) panelvar(AMC) dist(100) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dlog_ym
INDEPENDANT VARIABLES:  dA_soy dA_mze L4.rural_adult L4.log_y_pc_r L4.log_pop_area L4.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 100 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0241096   .0151143    -1.60   0.111    -.0537417    .0055225
      dA_mze |   .0139318   .0062174     2.24   0.025     .0017425    .0261212
             |
 rural_adult |
         L4. |  -.0144599   .0385244    -0.38   0.707    -.0899884    .0610686
             |
  log_y_pc_r |
         L4. |   -.107283   .0274247    -3.91   0.000    -.1610501   -.0535158
             |
log_pop_area |
         L4. |  -.0353255   .0052431    -6.74   0.000    -.0456047   -.0250463
             |
 alpha_adult |
         L4. |   .0929476   .0806202     1.15   0.249    -.0651112    .2510064
             |
    constant |     .82554   .0974626     8.47   0.000      .634461    1.016619
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA12e, ctitle("Change in Log wage (2010-2000)")         dec(3)  drop(constant) nonotes label asterisk(se) append excel
Tables\TableA12e.xml
dir : seeout

. 
. ****************************************************************************************************
. ****            f.   Conley's standard errors: cutoff at 200 Km                                 ****
. ****************************************************************************************************
. ols_spatial_HAC dLm_L   dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult constant if year == 2010 & rf ==1, lat(latitude) lon(longitude) timev
> ar(time) panelvar(AMC) dist(200) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dLm_L
INDEPENDANT VARIABLES:  dA_soy dA_mze L4.rural_adult L4.log_y_pc_r L4.log_pop_area L4.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0210396   .0057152     3.68   0.000     .0098348    .0322444
      dA_mze |  -.0042137   .0028729    -1.47   0.143    -.0098462    .0014187
             |
 rural_adult |
         L4. |   .0107396   .0099788     1.08   0.282    -.0088242    .0303034
             |
  log_y_pc_r |
         L4. |   .0018562   .0061116     0.30   0.761    -.0101259    .0138382
             |
log_pop_area |
         L4. |   .0016603   .0016501     1.01   0.314    -.0015747    .0048953
             |
 alpha_adult |
         L4. |   .0341938   .0206415     1.66   0.098    -.0062746    .0746623
             |
    constant |  -.0545047   .0218135    -2.50   0.013    -.0972709   -.0117385
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA12f, ctitle("Change in Employment share (2010-2000)") dec(3)  drop(constant) nonotes label asterisk(se) replace
dir : seeout

. ols_spatial_HAC dlog_Lm dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult constant if year == 2010 & rf ==1, lat(latitude) lon(longitude) timev
> ar(time) panelvar(AMC) dist(200) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dlog_Lm
INDEPENDANT VARIABLES:  dA_soy dA_mze L4.rural_adult L4.log_y_pc_r L4.log_pop_area L4.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |    .185629   .0371463     5.00   0.000     .1128024    .2584556
      dA_mze |  -.0428767   .0200677    -2.14   0.033    -.0822202   -.0035331
             |
 rural_adult |
         L4. |   .0505559   .0854442     0.59   0.554    -.1169606    .2180725
             |
  log_y_pc_r |
         L4. |   .0931167    .055969     1.66   0.096    -.0166127    .2028461
             |
log_pop_area |
         L4. |   .0199595   .0135746     1.47   0.142    -.0066541    .0465731
             |
 alpha_adult |
         L4. |   .1974533   .2026033     0.97   0.330    -.1997579    .5946645
             |
    constant |  -.6326233   .1943099    -3.26   0.001    -1.013575   -.2516717
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA12f, ctitle("Change in Log employment (2010-2000)")   dec(3)  drop(constant) nonotes label asterisk(se) append
dir : seeout

. ols_spatial_HAC dlog_ym dA_soy dA_mze l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult constant if year == 2010 & rf ==1, lat(latitude) lon(longitude) timev
> ar(time) panelvar(AMC) dist(200) dropvar bartlett
 
OLS REGRESSION
 
SE CORRECTED FOR CROSS-SECTIONAL SPATIAL DEPENDANCE
             AND PANEL-SPECIFIC SERIAL CORRELATION
 
DEPENDANT VARIABLE: dlog_ym
INDEPENDANT VARIABLES:  dA_soy dA_mze L4.rural_adult L4.log_y_pc_r L4.log_pop_area L4.alpha_adult constant
 
SPATIAL CORRELATION KERNAL CUTOFF: 200 KM
(NOTE: LINEAR BARTLETT WINDOW USED FOR SPATIAL KERNAL)
SERIAL CORRELATION KERNAL CUTOFF: 0 PERIODS
------------------------------------------------------------------------------
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0241096   .0175643    -1.37   0.170    -.0585451    .0103259
      dA_mze |   .0139318   .0072379     1.92   0.054    -.0002584     .028122
             |
 rural_adult |
         L4. |  -.0144599   .0420525    -0.34   0.731    -.0969053    .0679855
             |
  log_y_pc_r |
         L4. |   -.107283    .027493    -3.90   0.000    -.1611841   -.0533819
             |
log_pop_area |
         L4. |  -.0353255   .0057509    -6.14   0.000    -.0466004   -.0240506
             |
 alpha_adult |
         L4. |   .0929476   .0793162     1.17   0.241    -.0625547    .2484499
             |
    constant |     .82554   .1040236     7.94   0.000     .6215979    1.029482
------------------------------------------------------------------------------

.    outreg2 using Tables\TableA12f, ctitle("Change in Log wage (2010-2000)")         dec(3)  drop(constant) nonotes label asterisk(se) append excel
Tables\TableA12f.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table A13: The effect of technological change on agriculture and manufacturing             ****
. ****            Robustness to Alternative Definition of Technical Change                        ****
. ****************************************************************************************************
. reg dsoy_TA_w      dA_soy2 dA_mze2 l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year == 2006 & fs == 1, r

Linear regression                                      Number of obs =    3652
                                                       F(  6,  3645) =   49.99
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1502
                                                       Root MSE      =  .03903

------------------------------------------------------------------------------
             |               Robust
   dsoy_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     dA_soy2 |   .0201295    .002687     7.49   0.000     .0148613    .0253976
     dA_mze2 |  -.0008001   .0010561    -0.76   0.449    -.0028706    .0012704
             |
 rural_adult |
         L3. |    .037883   .0046467     8.15   0.000     .0287727    .0469934
             |
  log_y_pc_r |
         L3. |  -.0003843   .0021896    -0.18   0.861    -.0046772    .0039086
             |
log_pop_area |
         L3. |  -.0014043   .0005085    -2.76   0.006    -.0024014   -.0004073
             |
 alpha_adult |
         L3. |   .0560158   .0064566     8.68   0.000     .0433568    .0686747
             |
       _cons |  -.0622491   .0095377    -6.53   0.000    -.0809489   -.0435493
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA13, ctitle("Change in Soy area share")             dec(3) nocons nonotes replace label
dir : seeout

. reg dmze_TA_w      dA_soy2 dA_mze2 l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year == 2006 & fs == 1, r

Linear regression                                      Number of obs =    3652
                                                       F(  6,  3645) =    9.16
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0148
                                                       Root MSE      =  .06533

------------------------------------------------------------------------------
             |               Robust
   dmze_TA_w |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     dA_soy2 |    .001688   .0036476     0.46   0.644    -.0054635    .0088396
     dA_mze2 |   .0036234   .0015363     2.36   0.018     .0006113    .0066355
             |
 rural_adult |
         L3. |   .0097435   .0067656     1.44   0.150    -.0035213    .0230084
             |
  log_y_pc_r |
         L3. |  -.0050538    .003813    -1.33   0.185    -.0125296     .002422
             |
log_pop_area |
         L3. |   .0036772   .0006556     5.61   0.000     .0023918    .0049626
             |
 alpha_adult |
         L3. |  -.0089667   .0118379    -0.76   0.449    -.0321763    .0142428
             |
       _cons |   .0110699   .0151548     0.73   0.465    -.0186429    .0407827
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA13, ctitle("Change in Maize area share")       dec(3) nocons nonotes append  label
dir : seeout

. reg dlog_PQ_LA     dA_soy2 dA_mze2 l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year == 2006 & rf == 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =    7.27
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0112
                                                       Root MSE      =  .80658

------------------------------------------------------------------------------
             |               Robust
  dlog_PQ_LA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     dA_soy2 |   .1638908   .0377349     4.34   0.000     .0899101    .2378715
     dA_mze2 |  -.0411206   .0181902    -2.26   0.024    -.0767831   -.0054582
             |
 rural_adult |
         L3. |   .0979808   .0732651     1.34   0.181    -.0456582    .2416198
             |
  log_y_pc_r |
         L3. |  -.0118957   .0477594    -0.25   0.803    -.1055297    .0817383
             |
log_pop_area |
         L3. |  -.0170264   .0123541    -1.38   0.168     -.041247    .0071942
             |
 alpha_adult |
         L3. |  -.3416496   .1481933    -2.31   0.021    -.6321879   -.0511112
             |
       _cons |   .7540377   .1773208     4.25   0.000     .4063937    1.101682
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA13, ctitle("Change in Log value per worker") dec(3) nocons nonotes append  label
dir : seeout

. reg dlog_LA_TA     dA_soy2 dA_mze2 l3.rural_adult l3.log_y_pc_r l3.log_pop_area l3.alpha_adult if year == 2006 & rf == 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =    6.55
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0106
                                                       Root MSE      =  .54826

------------------------------------------------------------------------------
             |               Robust
  dlog_LA_TA |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     dA_soy2 |  -.1450428   .0290271    -5.00   0.000    -.2019515    -.088134
     dA_mze2 |   .0633578   .0134637     4.71   0.000     .0369617    .0897539
             |
 rural_adult |
         L3. |  -.1788131   .0496016    -3.60   0.000    -.2760589   -.0815674
             |
  log_y_pc_r |
         L3. |   .0400864   .0383078     1.05   0.295    -.0350175    .1151902
             |
log_pop_area |
         L3. |  -.0190073   .0103694    -1.83   0.067     -.039337    .0013223
             |
 alpha_adult |
         L3. |  -.0882908   .1164618    -0.76   0.448    -.3166184    .1400368
             |
       _cons |   .0413651   .1377871     0.30   0.764    -.2287716    .3115017
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA13, ctitle("Change in Log labor intensity")  dec(3) nocons nonotes append  label
dir : seeout

. reg dLa_L          dA_soy2 dA_mze2 l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   47.99
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0637
                                                       Root MSE      =  .07145

------------------------------------------------------------------------------
             |               Robust
       dLa_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     dA_soy2 |  -.0187795   .0034815    -5.39   0.000    -.0256052   -.0119539
     dA_mze2 |   .0048791   .0016365     2.98   0.003     .0016706    .0080876
             |
 rural_adult |
         L4. |  -.0688624   .0067698   -10.17   0.000    -.0821348   -.0555899
             |
  log_y_pc_r |
         L4. |   .0121214   .0042643     2.84   0.004     .0037612    .0204817
             |
log_pop_area |
         L4. |   .0004071   .0009725     0.42   0.675    -.0014995    .0023138
             |
 alpha_adult |
         L4. |  -.0071184   .0141109    -0.50   0.614    -.0347835    .0205466
             |
       _cons |  -.0701531   .0153553    -4.57   0.000    -.1002578   -.0400485
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA13, ctitle("Change in Employment share")       dec(3) nocons nonotes append  label
dir : seeout

. reg dLm_L          dA_soy2 dA_mze2 l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   40.90
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0499
                                                       Root MSE      =  .05548

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     dA_soy2 |   .0141093   .0030853     4.57   0.000     .0080605    .0201582
     dA_mze2 |  -.0012076   .0013978    -0.86   0.388    -.0039481    .0015328
             |
 rural_adult |
         L4. |   .0022537   .0051222     0.44   0.660    -.0077885     .012296
             |
  log_y_pc_r |
         L4. |   .0044935   .0031239     1.44   0.150    -.0016311    .0106182
             |
log_pop_area |
         L4. |   .0010307   .0007157     1.44   0.150    -.0003726    .0024339
             |
 alpha_adult |
         L4. |   .0295574   .0102139     2.89   0.004     .0095326    .0495822
             |
       _cons |  -.0469914   .0108577    -4.33   0.000    -.0682784   -.0257044
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA13, ctitle("Change in Employment share")       dec(3) nocons nonotes append  label
dir : seeout

. reg dlog_Lm        dA_soy2 dA_mze2 l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   45.30
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0556
                                                       Root MSE      =  .59092

------------------------------------------------------------------------------
             |               Robust
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     dA_soy2 |    .154929   .0280045     5.53   0.000     .1000251    .2098329
     dA_mze2 |  -.0296166   .0136246    -2.17   0.030    -.0563283    -.002905
             |
 rural_adult |
         L4. |  -.0172876   .0559638    -0.31   0.757    -.1270067    .0924315
             |
  log_y_pc_r |
         L4. |   .1109267   .0370065     3.00   0.003      .038374    .1834793
             |
log_pop_area |
         L4. |   .0154857   .0081567     1.90   0.058    -.0005058    .0314772
             |
 alpha_adult |
         L4. |    .149721   .1188271     1.26   0.208    -.0832439    .3826859
             |
       _cons |  -.5577237   .1282264    -4.35   0.000    -.8091163    -.306331
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA13, ctitle("Change in Log Employment")             dec(3) nocons nonotes append  label
dir : seeout

. reg dlog_ym        dA_soy2 dA_mze2 l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult if year == 2010 & rf == 1, r

Linear regression                                      Number of obs =    4149
                                                       F(  6,  4142) =   33.92
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0451
                                                       Root MSE      =  .35677

------------------------------------------------------------------------------
             |               Robust
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     dA_soy2 |   -.040493   .0161577    -2.51   0.012    -.0721707   -.0088153
     dA_mze2 |   .0227369    .007759     2.93   0.003     .0075251    .0379487
             |
 rural_adult |
         L4. |  -.0104439    .034342    -0.30   0.761    -.0777726    .0568848
             |
  log_y_pc_r |
         L4. |   -.106359   .0257486    -4.13   0.000    -.1568402   -.0558779
             |
log_pop_area |
         L4. |  -.0352634   .0047579    -7.41   0.000    -.0445914   -.0259353
             |
 alpha_adult |
         L4. |   .1001443    .074862     1.34   0.181    -.0466255     .246914
             |
       _cons |   .8144136   .0900357     9.05   0.000     .6378952    .9909319
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA13, ctitle("Change in Log wage")               dec(3) nocons nonotes append  label excel
Tables\TableA13.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table A7: The effect of agricultural technological change on manufacturing                 ****
. ****           Robustness to using a larger unit of observation: micro-regions                  ****
. ****************************************************************************************************
. use APST_micro, replace

. reg dLm_L   dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year==2010, r

Linear regression                                      Number of obs =     557
                                                       F(  6,   550) =   10.68
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1009
                                                       Root MSE      =  .03236

------------------------------------------------------------------------------
             |               Robust
       dLm_L |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .0167888   .0038602     4.35   0.000     .0092062    .0243714
      dA_mze |  -.0025092   .0015904    -1.58   0.115    -.0056332    .0006149
             |
 rural_adult |
         L4. |   .0139679   .0121768     1.15   0.252    -.0099509    .0378866
             |
  log_y_pc_r |
         L4. |  -.0015054   .0072828    -0.21   0.836     -.015811    .0128002
             |
log_pop_area |
         L4. |   .0040767   .0010409     3.92   0.000      .002032    .0061214
             |
 alpha_adult |
         L4. |   .0163367    .021426     0.76   0.446    -.0257501    .0584236
             |
       _cons |  -.0414214   .0264989    -1.56   0.119    -.0934728      .01063
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA7, ctitle("Change in Employment share (2010-2000)") dec(3) nocons  replace
dir : seeout

. reg dlog_Lm dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year==2010, r

Linear regression                                      Number of obs =     557
                                                       F(  6,   550) =   11.80
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.1072
                                                       Root MSE      =  .29717

------------------------------------------------------------------------------
             |               Robust
     dlog_Lm |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |   .1391064   .0287677     4.84   0.000     .0825984    .1956143
      dA_mze |   -.036725   .0128351    -2.86   0.004    -.0619368   -.0115132
             |
 rural_adult |
         L4. |   .0171223   .1212016     0.14   0.888    -.2209523     .255197
             |
  log_y_pc_r |
         L4. |    .058223   .0884313     0.66   0.511    -.1154814    .2319274
             |
log_pop_area |
         L4. |   .0302359   .0107359     2.82   0.005     .0091475    .0513243
             |
 alpha_adult |
         L4. |    .007073   .2611073     0.03   0.978    -.5058166    .5199626
             |
       _cons |  -.3333791   .2820399    -1.18   0.238    -.8873862    .2206281
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA7, ctitle("Change in Log employment (2010-2000)")   dec(3) nocons  append
dir : seeout

. reg dlog_ym dA_soy dA_mze  l4.rural_adult l4.log_y_pc_r l4.log_pop_area l4.alpha_adult  if year==2010, r

Linear regression                                      Number of obs =     557
                                                       F(  6,   550) =   33.12
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.2394
                                                       Root MSE      =  .16539

------------------------------------------------------------------------------
             |               Robust
     dlog_ym |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      dA_soy |  -.0219373   .0162921    -1.35   0.179    -.0539396    .0100651
      dA_mze |   .0164353   .0066768     2.46   0.014     .0033201    .0295505
             |
 rural_adult |
         L4. |  -.1032834   .0888686    -1.16   0.246    -.2778468      .07128
             |
  log_y_pc_r |
         L4. |  -.1681975   .0731513    -2.30   0.022    -.3118876   -.0245074
             |
log_pop_area |
         L4. |  -.0315681   .0072331    -4.36   0.000    -.0457759   -.0173602
             |
 alpha_adult |
         L4. |    .127856   .1798181     0.71   0.477    -.2253583    .4810703
             |
       _cons |   1.069682    .236342     4.53   0.000      .605439    1.533926
------------------------------------------------------------------------------

.         outreg2 using Tables\TableA7, ctitle("Change in Log wage (2010-2000)")         dec(3) nocons  append excel
Tables\TableA7.xml
dir : seeout

. 
. ****************************************************************************************************
. **** Table A9: The effect of agricultural technological change on manufacturing                 ****
. ****           Robustness of results reported in Table 9 to controlling for commodity prices    ****
. ****************************************************************************************************
. * The data used for the regressions reported on Table A9 has been provided by the Brazilian Statistical 
. * Office and is confidential. It therefore cannot be posted on the AER website. Here we post the code 
. * used to estimate the regressions reported in table A9. We explain data construction in the appendix.
. 
. /* 
> use PIA Database 
> 
> gen     A_soy         = cond(year <  2003,A_soy_l, ///
>                         cond(year >= 2003,A_soy_h,.))
> gen     A_mze         = cond(year <  2003,A_mze_l, ///
>                         cond(year >= 2003,A_mze_h,.))
> gen     pA_soy        = p_soybean  * A_soy_l
> gen     pA_mze        = p_maize    * A_mze_l
> 
> for X in var rural_adult log_y_pc_r log_pop_area alpha_adult :  for Y in num 1996/2007 : replace X91_t = X91_t * (Y-1995) if year == Y
> 
> xi: areg log_L_end_ul_manuf_P30 A_soy A_mze               rural_adult91_t                                                        i.year if year < 2008, absorb(AMC) clu
> ster(AMC)
>         outreg2 using RF-PIA, dec(3) nocons ctitle(" ")                    replace drop(_Iy*)
> xi: areg log_L_end_ul_manuf_P30 A_soy A_mze pA_soy pA_mze rural_adult91_t                                                        i.year if year < 2008, absorb(AMC) clu
> ster(AMC)
>         outreg2 using RF-PIA, dec(3) nocons ctitle("Log Total Employment") append  drop(_Iy*)
> xi: areg log_L_end_ul_manuf_P30 A_soy A_mze pA_soy pA_mze rural_adult91_t       alpha_adult91_t log_pop_area91_t log_y_pc_r91_t  i.year if year < 2008, absorb(AMC) clu
> ster(AMC)
>         outreg2 using RF-PIA, dec(3) nocons ctitle(" ")                    append  drop(_Iy*)
> xi: areg log_W_L_ul_manuf_P30   A_soy A_mze               rural_adult91_t                                                        i.year if year < 2008, absorb(AMC) clu
> ster(AMC)
>         outreg2 using RF-PIA, dec(3) nocons ctitle(" ")                    append  drop(_Iy*)
> xi: areg log_W_L_ul_manuf_P30   A_soy A_mze pA_soy pA_mze rural_adult91_t                                                        i.year if year < 2008, absorb(AMC) clu
> ster(AMC)
>         outreg2 using RF-PIA, dec(3) nocons ctitle("Log Wage")             append  drop(_Iy*)
> xi: areg log_W_L_ul_manuf_P30   A_soy A_mze pA_soy pA_mze rural_adult91_t       alpha_adult91_t log_pop_area91_t log_y_pc_r91_t  i.year if year < 2008, absorb(AMC) clu
> ster(AMC)        
>         outreg2 using RF-PIA, dec(3) nocons ctitle(" ")                    append  drop(_Iy*) sortvar(A_soy A_mze rural_adult91_t)      
> */
.         
. log close       
      name:  <unnamed>
       log:  D:\Dropbox\Industry_Brazil\Final_Submission\APST_ReplicationFiles\APST_replication tables.log
  log type:  text
 closed on:  15 Dec 2015, 17:52:33
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
